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101-528: The government's proposed solution to these problems is to introduce a wide-ranging regime of Internet regulation in the United Kingdom, enforcing codes of practice on Internet companies, which would be subject to a statutory duty of care , and the threat of punishment or blocking if the codes are not complied with. Following the abandonment of the proposed UK Internet age verification system in October 2019,

202-527: A filter bubble and being unaware of important or useful content. Corporate algorithms could be skewed to invisibly favor financial arrangements or agreements between companies, without the knowledge of a user who may mistake the algorithm as being impartial. For example, American Airlines created a flight-finding algorithm in the 1980s. The software presented a range of flights from various airlines to customers, but weighed factors that boosted its own flights, regardless of price or convenience. In testimony to

303-439: A 2012 study showed that names commonly associated with blacks were more likely to yield search results implying arrest records, regardless of whether there is any police record of that individual's name. A 2015 study also found that Black and Asian people are assumed to have lesser functioning lungs due to racial and occupational exposure data not being incorporated into the prediction algorithm's model of lung function. In 2019,

404-925: A decree setting out an intention to curb arbitrary removal of social media accounts through new legislation. Judges in Brazil have also ordered blocks on a number of social media and social networking platforms including Telegram , WhatsApp , and Twitter . In June 2010, the Fijian Government passed the Media Industry Development Decree of 2010 establishing the Media Industry Development Authority of Fiji which enforces media ethics governing all media organizations in Fiji . The Authority has implemented penalties which includes fines and imprisonment in case of any ethical breaches. The aim of

505-431: A form of media policy with rules enforced by the jurisdiction of law. Guidelines for mass media use differ across the world. This regulation , via law , rules or procedures, can have various goals, for example intervention to protect a stated " public interest ", or encouraging competition and an effective media market , or establishing common technical standards. The principal targets of mass media regulation are

606-578: A high number of arrests in a particular area, an algorithm may assign more police patrols to that area, which could lead to more arrests. The decisions of algorithmic programs can be seen as more authoritative than the decisions of the human beings they are meant to assist, a process described by author Clay Shirky as "algorithmic authority". Shirky uses the term to describe "the decision to regard as authoritative an unmanaged process of extracting value from diverse, untrustworthy sources", such as search results. This neutrality can also be misrepresented by

707-485: A military coup ousted Aung San Suu Kyi. Lowstedt and Al-Wahid suggested that the authority need to issue diverse media laws centering at anti- monopoly and anti- oligopoly with democratic legitimacy since media outlets are important for national security and social stability. The global regulation of new media technologies is to ensure the cultural diversity in media content, and provide a free space of public access and various opinions and ideas without censorship. Also,

808-530: A much smaller number of outlets but is the only press regulator recognised by the PRP (since October 2016). Ofcom also oversees the use of social media and devices in the United Kingdom. BBC reports that Ofcom analyzes media use of the youth (ages 3 to 15 years old) to gather information of how the United Kingdom utilizes their media. Broadcast media (TV, radio, video on demand ), telecommunications, and postal services are regulated by Ofcom . The First Amendment to

909-526: A new computer-guidance assessment system that denied entry to women and men with "foreign-sounding names" based on historical trends in admissions. While many schools at the time employed similar biases in their selection process, St. George was most notable for automating said bias through the use of an algorithm, thus gaining the attention of people on a much wider scale. In recent years, when more algorithms started to use machine learning methods on real world data, algorithmic bias can be found more often due to

1010-412: A new form of "generative power", in that they are a virtual means of generating actual ends. Where previously human behavior generated data to be collected and studied, powerful algorithms increasingly could shape and define human behaviors. Concerns over the impact of algorithms on society have led to the creation of working groups in organizations such as Google and Microsoft , which have co-created

1111-514: A person's race, nationality, ethnicity, and religion. In addition, a Voluntary Code of Conduct was passed in 2016 to counter hate speech online. European countries could also request a removal of content in other countries so long as they deemed it as a form of "terrorist" content To control personal data of European citizens, the EU General Data Protection Regulation (GDPR) was passed on May 25, 2018 On April 23, 2022,

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1212-570: A personal blog or website under supervision. More than 500 websites have already been blocked in Egypt prior to the new law in 2018. Websites must go through Egypt's “Supreme Council for the Administration of the Media” to acquire a license to publish a website. Media regulation in Egypt has always been limited, but as in recent years, it has become even more limited. In 2018, a law was put in place to prevent

1313-828: A research study revealed that a healthcare algorithm sold by Optum favored white patients over sicker black patients. The algorithm predicts how much patients would cost the health-care system in the future. However, cost is not race-neutral, as black patients incurred about $ 1,800 less in medical costs per year than white patients with the same number of chronic conditions, which led to the algorithm scoring white patients as equally at risk of future health problems as black patients who suffered from significantly more diseases. A study conducted by researchers at UC Berkeley in November 2019 revealed that mortgage algorithms have been discriminatory towards Latino and African Americans which discriminated against minorities based on "creditworthiness" which

1414-560: A result of design. For example, algorithms that determine the allocation of resources or scrutiny (such as determining school placements) may inadvertently discriminate against a category when determining risk based on similar users (as in credit scores). Meanwhile, recommendation engines that work by associating users with similar users, or that make use of inferred marketing traits, might rely on inaccurate associations that reflect broad ethnic, gender, socio-economic, or racial stereotypes. Another example comes from determining criteria for what

1515-810: A result of pre-existing cultural, social, or institutional expectations; by how features and labels are chosen; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. Algorithmic bias has been cited in cases ranging from election outcomes to the spread of online hate speech . It has also arisen in criminal justice, healthcare, and hiring, compounding existing racial, socioeconomic, and gender biases. The relative inability of facial recognition technology to accurately identify darker-skinned faces has been linked to multiple wrongful arrests of black men, an issue stemming from imbalanced datasets. Problems in understanding, researching, and discovering algorithmic bias persist due to

1616-549: A rival candidate. Facebook users who saw messages related to voting were more likely to vote. A 2010 randomized trial of Facebook users showed a 20% increase (340,000 votes) among users who saw messages encouraging voting, as well as images of their friends who had voted. Legal scholar Jonathan Zittrain has warned that this could create a "digital gerrymandering" effect in elections, "the selective presentation of information by an intermediary to meet its agenda, rather than to serve its users", if intentionally manipulated. In 2016,

1717-505: A strong tendency towards male defaults. In particular, this is observed in fields linked to unbalanced gender distribution, including STEM occupations. In fact, current machine translation systems fail to reproduce the real world distribution of female workers. In 2015, Amazon.com turned off an AI system it developed to screen job applications when they realized it was biased against women. The recruitment tool excluded applicants who attended all-women's colleges and resumes that included

1818-447: A worker that previously did the job the algorithm is going to do from now on). Bias can be introduced to an algorithm in several ways. During the assemblage of a dataset, data may be collected, digitized, adapted, and entered into a database according to human-designed cataloging criteria. Next, programmers assign priorities, or hierarchies , for how a program assesses and sorts that data. This requires human decisions about how data

1919-701: A working group named Fairness, Accountability, and Transparency in Machine Learning. Ideas from Google have included community groups that patrol the outcomes of algorithms and vote to control or restrict outputs they deem to have negative consequences. In recent years, the study of the Fairness, Accountability, and Transparency (FAT) of algorithms has emerged as its own interdisciplinary research area with an annual conference called FAccT. Critics have suggested that FAT initiatives cannot serve effectively as independent watchdogs when many are funded by corporations building

2020-468: Is categorized, and which data is included or discarded. Some algorithms collect their own data based on human-selected criteria, which can also reflect the bias of human designers. Other algorithms may reinforce stereotypes and preferences as they process and display "relevant" data for human users, for example, by selecting information based on previous choices of a similar user or group of users. Beyond assembling and processing data, bias can emerge as

2121-862: Is included and excluded from results. These criteria could present unanticipated outcomes for search results, such as with flight-recommendation software that omits flights that do not follow the sponsoring airline's flight paths. Algorithms may also display an uncertainty bias , offering more confident assessments when larger data sets are available. This can skew algorithmic processes toward results that more closely correspond with larger samples, which may disregard data from underrepresented populations. The earliest computer programs were designed to mimic human reasoning and deductions, and were deemed to be functioning when they successfully and consistently reproduced that human logic. In his 1976 book Computer Power and Human Reason , artificial intelligence pioneer Joseph Weizenbaum suggested that bias could arise both from

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2222-588: Is more deeply integrated into society. Apart from exclusion, unanticipated uses may emerge from the end user relying on the software rather than their own knowledge. In one example, an unanticipated user group led to algorithmic bias in the UK, when the British National Act Program was created as a proof-of-concept by computer scientists and immigration lawyers to evaluate suitability for British citizenship . The designers had access to legal expertise beyond

2323-565: Is most concerned with algorithms that reflect "systematic and unfair" discrimination. This bias has only recently been addressed in legal frameworks, such as the European Union's General Data Protection Regulation (proposed 2018) and the Artificial Intelligence Act (proposed 2021, approved 2024). As algorithms expand their ability to organize society, politics, institutions, and behavior, sociologists have become concerned with

2424-510: Is no single "algorithm" to examine, but a network of many interrelated programs and data inputs, even between users of the same service. Algorithms are difficult to define , but may be generally understood as lists of instructions that determine how programs read, collect, process, and analyze data to generate output. For a rigorous technical introduction, see Algorithms . Advances in computer hardware have led to an increased ability to process, store and transmit data. This has in turn boosted

2525-579: Is regarded as a problem for the democratic process when the commercial news media fail to provide balanced and thorough coverage of political issues and debates. Many countries in Europe and Japan have implemented publicly funded media with public service obligations in order to meet the needs that are not satisfied by free commercial media. However, the public service media are under increasing pressure due to competition from commercial media, as well as political pressure. Other countries, including

2626-498: Is responsible for their exclusion. Similarly, problems may emerge when training data (the samples "fed" to a machine, by which it models certain conclusions) do not align with contexts that an algorithm encounters in the real world. In 1990, an example of emergent bias was identified in the software used to place US medical students into residencies, the National Residency Match Program (NRMP). The algorithm

2727-692: Is software that relies on randomness for fair distributions of results. If the random number generation mechanism is not truly random, it can introduce bias, for example, by skewing selections toward items at the end or beginning of a list. A decontextualized algorithm uses unrelated information to sort results, for example, a flight-pricing algorithm that sorts results by alphabetical order would be biased in favor of American Airlines over United Airlines. The opposite may also apply, in which results are evaluated in contexts different from which they are collected. Data may be collected without crucial external context: for example, when facial recognition software

2828-495: Is the British Nationality Act Program, designed to automate the evaluation of new British citizens after the 1981 British Nationality Act . The program accurately reflected the tenets of the law, which stated that "a man is the father of only his legitimate children, whereas a woman is the mother of all her children, legitimate or not." In its attempt to transfer a particular logic into an algorithmic process,

2929-410: Is the peaceful environment of diversity of editorial ownership and free speech. White Paper No.57 claimed that real content diversity can only be attained by a pluralistically owned and independent editorial media whose production is founded on the principles of journalistic professionalism. To ensure this diversity, Norwegian government regulates the framework conditions of the media and primarily focuses

3030-450: Is used by surveillance cameras, but evaluated by remote staff in another country or region, or evaluated by non-human algorithms with no awareness of what takes place beyond the camera's field of vision . This could create an incomplete understanding of a crime scene, for example, potentially mistaking bystanders for those who commit the crime. Lastly, technical bias can be created by attempting to formalize decisions into concrete steps on

3131-618: The Culture Secretary Nicky Morgan stated that the government would seek to follow the White Paper's approach to regulation as an alternative. This Internet-related article is a stub . You can help Misplaced Pages by expanding it . This article related to the politics of the United Kingdom , or its predecessor or constituent states, is a stub . You can help Misplaced Pages by expanding it . Internet regulation Mass media regulations or simply media regulations are

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3232-484: The United States Congress , the president of the airline stated outright that the system was created with the intention of gaining competitive advantage through preferential treatment. In a 1998 paper describing Google , the founders of the company had adopted a policy of transparency in search results regarding paid placement, arguing that "advertising-funded search engines will be inherently biased towards

3333-602: The predictive policing software (PredPol), deployed in Oakland, California, suggested an increased police presence in black neighborhoods based on crime data reported by the public. The simulation showed that the public reported crime based on the sight of police cars, regardless of what police were doing. The simulation interpreted police car sightings in modeling its predictions of crime, and would in turn assign an even larger increase of police presence within those neighborhoods. The Human Rights Data Analysis Group , which conducted

3434-478: The press , radio and television , but may also include film , recorded music, cable, satellite, storage and distribution technology (discs, tapes etc.), the internet , mobile phones etc. It includes the regulation of independent media . The transmission of content and intellectual property have attracted attention and regulation from authorities worldwide, due to the memetic nature and possible social impact of content sharing. The regulation of content may take

3535-410: The "label choice bias" aim to match the actual target (what the algorithm is predicting) more closely to the ideal target (what researchers want the algorithm to predict), so for the prior example, instead of predicting cost, researchers would focus on the variable of healthcare needs which is rather more significant. Adjusting the target led to almost double the number of Black patients being selected for

3636-485: The 1960s. The rise of the advertising industry helped the most powerful newspapers grow increasingly, while the little publications were struggling at the bottom of the market. Because of the lack of diversity in the newspaper industry, the Norwegian Government took action, affecting the true freedom of speech. In 1969, Norwegian government started to provide press subsidies to small local newspapers. But this method

3737-748: The BNAP inscribed the logic of the British Nationality Act into its algorithm, which would perpetuate it even if the act was eventually repealed. Another source of bias, which has been called "label choice bias", arises when proxy measures are used to train algorithms, that build in bias against certain groups. For example, a widely-used algorithm predicted health care costs as a proxy for health care needs, and used predictions to allocate resources to help patients with complex health needs. This introduced bias because Black patients have lower costs, even when they are just as unhealthy as White patients Solutions to

3838-464: The Chinese people and controlled the media, making the media highly political. The economic reform decreased the governing function of media and created a tendency for mass media to stand for the society but not only authority. The previous unbalanced structure between powered government and weak society was loosed by the policy in some level, but not truly changed until the emergence of Internet. At first

3939-738: The Communications Act worked to create the Federal Communications Commission (FCC) in the United States. The FCC is a federal agency that works to regulate interstate and foreign communications. They are given the power to make legal decisions and judgments about regulation content under the Communications Satellite Act of 1962 , including the regulation of cable television operation, telegraph, telephone, two-way radio and radio operators, satellite communication and

4040-581: The European Parliament and Council established a political agreement on the new rules. The media systems in Scandinavian countries are twin-duopolistic with powerful public service broadcasting and periodic strong government intervention. Hallin and Mancini introduced the Norwegian media system as Democratic Corporatist. Newspapers started early and developed very well without state regulation until

4141-441: The European Union's General Data Protection Regulation which sets limits on the information collected by Internet giants and corporations for sale and use in analytics. These require the balance between rights and obligations. To maintain the contractual balance, society expects the media to take their privilege responsibly. Besides, market forces failed to guarantee the wide range of public opinions and free expression. Intend to

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4242-650: The US, have weak or non-existing public service media. Egypt 's regulation laws encompass media and journalism publishing. Any form of press release to the public that goes against the Egyptian Constitution can be subject to punishment by these laws. This law was put in place to regulate the circulation of misinformation online. Legal action can be taken on those who share false facts. Egypt 's Supreme Council for Media Regulations (SCMR) will be authorised to place people with more than 5,000 followers on social media or with

4343-511: The United States Constitution forbids the government from abridging freedom of speech or freedom of the press. However, there are certain exceptions to free speech. For example, there are regulations on public broadcasters: the Federal Communications Commission forbids the broadcast of "indecent" material on the public airwaves. The accidental exposure of Janet Jackson 's nipple during the halftime show at Super Bowl XXXVIII led to

4444-457: The advertisers and away from the needs of the consumers." This bias would be an "invisible" manipulation of the user. A series of studies about undecided voters in the US and in India found that search engine results were able to shift voting outcomes by about 20%. The researchers concluded that candidates have "no means of competing" if an algorithm, with or without intent, boosted page listings for

4545-610: The algorithm weighed the location choices of the higher-rated partner first. The result was a frequent assignment of highly preferred schools to the first partner and lower-preferred schools to the second partner, rather than sorting for compromises in placement preference. Additional emergent biases include: Unpredictable correlations can emerge when large data sets are compared to each other. For example, data collected about web-browsing patterns may align with signals marking sensitive data (such as race or sexual orientation). By selecting according to certain behavior or browsing patterns,

4646-419: The assumption that human behavior works in the same way. For example, software weighs data points to determine whether a defendant should accept a plea bargain, while ignoring the impact of emotion on a jury. Another unintended result of this form of bias was found in the plagiarism-detection software Turnitin , which compares student-written texts to information found online and returns a probability score that

4747-423: The bias existing in the data. Though well-designed algorithms frequently determine outcomes that are equally (or more) equitable than the decisions of human beings, cases of bias still occur, and are difficult to predict and analyze. The complexity of analyzing algorithmic bias has grown alongside the complexity of programs and their design. Decisions made by one designer, or team of designers, may be obscured among

4848-464: The code could incorporate the programmer's imagination of how the world works, including their biases and expectations. While a computer program can incorporate bias in this way, Weizenbaum also noted that any data fed to a machine additionally reflects "human decisionmaking processes" as data is being selected. Finally, he noted that machines might also transfer good information with unintended consequences if users are unclear about how to interpret

4949-542: The complex interplay between the grammatical properties of a language and real-world biases that can become embedded in AI systems, potentially perpetuating harmful stereotypes and assumptions. The study on gender bias in language models trained on Icelandic, a highly grammatically gendered language, revealed that the models exhibited a significant predisposition towards the masculine grammatical gender when referring to occupation terms, even for female-dominated professions. This suggests

5050-441: The constitution and are able to report freely Many media outlets in Brazil are owned or invested in by its politicians that have an influence on their editorial decisions. Much of Brazil’s media regulations change with their change in government, the current government has had very little expansion of laws regarding media regulation past freedom of speech guaranteed in their constitution. In 2021, President Jair Bolsonaro signed

5151-501: The context of global information exchange via the Internet. Restrictions that vary between jurisdictions exist that focus on ceasing the broadcasting of specific forms of content. This may include content that has a specific moral standard or "non-mainstream" viewpoints. About 48 countries have taken legislative or administrative steps to regulate technology companies and the content that goes along with them. The regulations work to temperate

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5252-417: The data back into itself in the event individuals become registered criminals, further enforcing the bias created by the dataset the algorithm is acting on. Recommender systems such as those used to recommend online videos or news articles can create feedback loops. When users click on content that is suggested by algorithms, it influences the next set of suggestions. Over time this may lead to users entering

5353-567: The data on which these models are trained. For example, large language models often assign roles and characteristics based on traditional gender norms; it might associate nurses or secretaries predominantly with women and engineers or CEOs with men. Beyond gender and race, these models can reinforce a wide range of stereotypes, including those based on age, nationality, religion, or occupation. This can lead to outputs that unfairly generalize or caricature groups of people, sometimes in harmful or derogatory ways. A recent focus in research has been on

5454-428: The data used in a program, but also from the way a program is coded. Weizenbaum wrote that programs are a sequence of rules created by humans for a computer to follow. By following those rules consistently, such programs "embody law", that is, enforce a specific way to solve problems. The rules a computer follows are based on the assumptions of a computer programmer for how these problems might be solved. That means

5555-527: The decree is to promote a balance, fair and accurate reporting in Fiji. Indonesian Ministerial Regulation #5 (MR5) grants the Ministry of Communication and Information Technology the authority to compel any individual, business entity or community that operates "electronic systems" (ESOs) to restrict or remove any content deemed to be in violation of Indonesia's laws within 24 hours. The breadth and open-ended nature of

5656-462: The design and adoption of technologies such as machine learning and artificial intelligence . By analyzing and processing data, algorithms are the backbone of search engines, social media websites, recommendation engines, online retail, online advertising, and more. Contemporary social scientists are concerned with algorithmic processes embedded into hardware and software applications because of their political and social impact, and question

5757-474: The end effect would be almost identical to discrimination through the use of direct race or sexual orientation data. In other cases, the algorithm draws conclusions from correlations, without being able to understand those correlations. For example, one triage program gave lower priority to asthmatics who had pneumonia than asthmatics who did not have pneumonia. The program algorithm did this because it simply compared survival rates: asthmatics with pneumonia are at

5858-465: The end users in immigration offices, whose understanding of both software and immigration law would likely have been unsophisticated. The agents administering the questions relied entirely on the software, which excluded alternative pathways to citizenship, and used the software even after new case laws and legal interpretations led the algorithm to become outdated. As a result of designing an algorithm for users assumed to be legally savvy on immigration law,

5959-452: The expectation and ensurance, regulation over the media formalized. Commercial mass media controlled by economic market forces are not always delivering a product that satisfies all needs. Children and minority interests are not always serviced well. Political news are often trivialized and reduced to tabloid journalism , slogans , sound bites , spin , horse race reporting , celebrity scandals , populism , and infotainment . This

6060-403: The form of selective censorship of works and content most often featuring obscenity , violence , or dissent , with wide variation through time and geographical situation concerning the bounds of legal content transmission. Content regulation also concerns the rules regarding transmission of the content itself. Regulations on content vary, and may come into conflict with each other more often in

6161-480: The government authorities to be able to block the content and those who want to be able to produce content, or be able to publish a website, have to obtain a license. In order to do that, those would need to go to Egypt's “Supreme Council for the Administration of the Media.” At the early period of the modern history of China, the relationship between government and society was extremely unbalanced. Government held power over

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6262-445: The highest risk. Historically, for this same reason, hospitals typically give such asthmatics the best and most immediate care. Emergent bias can occur when an algorithm is used by unanticipated audiences. For example, machines may require that users can read, write, or understand numbers, or relate to an interface using metaphors that they do not understand. These exclusions can become compounded, as biased or exclusionary technology

6363-552: The intended function of the algorithm. Bias can emerge from many factors, including but not limited to the design of the algorithm or the unintended or unanticipated use or decisions relating to the way data is coded, collected, selected or used to train the algorithm. For example, algorithmic bias has been observed in search engine results and social media platforms . This bias can have impacts ranging from inadvertent privacy violations to reinforcing social biases of race, gender, sexuality, and ethnicity. The study of algorithmic bias

6464-541: The internet. The FCC helps to maintain many areas regarding regulation which includes fair competition, media responsibility, public safety, and homeland security. Content on the Internet is also monitored in the United States by federal law enforcement and intelligence agencies such as the CIA using the provisions of the Patriot Act among other acts of legislation, to profile interactions between users and content, and to restrict

6565-526: The language used by experts and the media when results are presented to the public. For example, a list of news items selected and presented as "trending" or "popular" may be created based on significantly wider criteria than just their popularity. Because of their convenience and authority, algorithms are theorized as a means of delegating responsibility away from humans. This can have the effect of reducing alternative options, compromises, or flexibility. Sociologist Scott Lash has critiqued algorithms as

6666-430: The laws regarding content admissibility are designed to suppress content that is relative to the government and harmful content towards users. The use of artificial intelligence (AI) technology and algorithms is in use to flag and remove inappropriate content, with possible abuses and algorithmic bias . Over the years, content regulation has been put in place to protect and promote human rights and digital rights, such as

6767-450: The many pieces of code created for a single program; over time these decisions and their collective impact on the program's output may be forgotten. In theory, these biases may create new patterns of behavior, or "scripts", in relationship to specific technologies as the code interacts with other elements of society. Biases may also impact how society shapes itself around the data points that algorithms require. For example, if data shows

6868-671: The media sector is to ensure freedom of speech , structural pluralism , national language and culture and the protection of children from harmful media content. Relative regulatory incentives includes the Media Ownership Law, the Broadcasting Act, and the Editorial Independence Act. NOU 1988:36 stated that a fundamental premise of all Norwegian media regulation is that news media serves as an oppositional force to power. The condition for news media to achieve this role

6969-465: The models amplified societal gender biases present in the training data. Political bias refers to the tendency of algorithms to systematically favor certain political viewpoints, ideologies, or outcomes over others. Language models may also exhibit political biases. Since the training data includes a wide range of political opinions and coverage, the models might generate responses that lean towards particular political ideologies or viewpoints, depending on

7070-565: The passage of the Broadcast Decency Enforcement Act of 2005 which increased the maximum fine that the FCC could level for indecent broadcasts from $ 32,500 to $ 325,000—with a maximum liability of $ 3 million. This is to shield younger individuals from expressions and ideas that are deemed offensive. The Supreme Court of the United States has yet to touch the internet, but that could change if net neutrality comes into play. In 1934,

7171-562: The past, data can often contain hidden biases. For example, black people are likely to receive longer sentences than white people who committed the same crime. This could potentially mean that a system amplifies the original biases in the data. In 2015, Google apologized when a couple of black users complained that an image-identification algorithm in its Photos application identified them as gorillas . In 2010, Nikon cameras were criticized when image-recognition algorithms consistently asked Asian users if they were blinking. Such examples are

7272-463: The preference of some individuals. In the field of media, relative legislation must be introduced as soon as possible and applied strictly to avoid the case that some leaders overwhelm the law with their power to control the media content. Algorithmic bias Algorithmic bias describes systematic and repeatable errors in a computer system that create " unfair " outcomes, such as "privileging" one category over another in ways different from

7373-542: The press and any media outlet from putting out content that violates the Egyptian Constitution, and/or contain any “violence, racism, hatred, or extremism.” If any content causes national security concerns or is broadcast as ‘false news’, the Egyptian Government will put a ban on those media outlets that produced that media. The law known as ‘The SCMR Law’, creates a media regulatory restriction plan that allows

7474-417: The prevalence of those views in the data. Technical bias emerges through limitations of a program, computational power, its design, or other constraint on the system. Such bias can also be a restraint of design, for example, a search engine that shows three results per screen can be understood to privilege the top three results slightly more than the next three, as in an airline price display. Another case

7575-413: The problem of convergence and concentration of media. The Digital Services Function (DSA) governs the responsibilities of digital services that act as mediators between customers and goods, services, and content. This comprises, for example, internet marketplaces. To reduce hate crime and speech , the 2008 Framework Decision deemed that it is illegal to encourage and spread any form of hatred based on

7676-458: The product of bias in biometric data sets. Biometric data is drawn from aspects of the body, including racial features either observed or inferred, which can then be transferred into data points. Speech recognition technology can have different accuracies depending on the user's accent. This may be caused by the a lack of training data for speakers of that accent. Biometric data about race may also be inferred, rather than observed. For example,

7777-765: The production and dissemination of dissenting content such as whistleblowing information. Content on the Internet is also monitored in the United States by federal law enforcement and intelligence agencies such as the CIA using the provisions of the Patriot Act among other acts of legislation, to profile interactions between users and content, and to restrict the production and dissemination of dissenting content such as whistleblowing information. Brazil’s constitution , written in 1988, guarantees freedom of expression without censorship. It also protects privacy of communications unless by court order. Journalists in Brazil are protected under

7878-437: The professional networking site LinkedIn was discovered to recommend male variations of women's names in response to search queries. The site did not make similar recommendations in searches for male names. For example, "Andrea" would bring up a prompt asking if users meant "Andrew", but queries for "Andrew" did not ask if users meant to find "Andrea". The company said this was the result of an analysis of users' interactions with

7979-466: The program. Machine learning bias refers to systematic and unfair disparities in the output of machine learning algorithms. These biases can manifest in various ways and are often a reflection of the data used to train these algorithms. Here are some key aspects: Language bias refers a type of statistical sampling bias tied to the language of a query that leads to "a systematic deviation in sampling information that prevents it from accurately representing

8080-415: The proprietary nature of algorithms, which are typically treated as trade secrets. Even when full transparency is provided, the complexity of certain algorithms poses a barrier to understanding their functioning. Furthermore, algorithms may change, or respond to input or output in ways that cannot be anticipated or easily reproduced for analysis. In many cases, even within a single website or application, there

8181-877: The regulation on pluralistic ownership. Following the Leveson Inquiry the Press Recognition Panel (PRP) was set up under the Royal Charter on self-regulation of the press to judge whether press regulators meet the criteria recommended by the Leveson Inquiry for recognition under the Charter. By 2016 the UK had two new press regulatory bodies: the Independent Press Standards Organisation (IPSO), which regulates most national newspapers and many other media outlets; and IMPRESS , which regulates

8282-470: The regulation protects the independence of media ownership from dominance of powerful financial corporations, and preserves the media from commercial and political hegemony. In China, the possibility that a film approved by Central Board of Film Censors can be banned due to the disagreement of a specific leading cadre has never been eliminated. The Chinese screenwriter Wang Xingdong stated that regulation over literature and art should be based on laws and not

8383-459: The regulation, implemented by the ministry in November 2020, can lead to censorship. The Myanmar government drafted a law in February 2021 that would empower authorities to "order internet shutdowns, disrupt or block online services, ban service providers, intercept user accounts, access personal data of users and force the removal of any content on demand." The "cybersecurity law" was drafted after

8484-478: The regulator did not regard Internet as a category of mass media but a technique of business. Underestimating the power of the internet as a communications tool resulted in a lack of internet regulation. Since then, the internet has changed communication methods, media structure and overthrown the pattern of public voice expression in China. Regulators have not and would not let the Internet out of control. In recent years,

8585-617: The results. Weizenbaum warned against trusting decisions made by computer programs that a user doesn't understand, comparing such faith to a tourist who can find his way to a hotel room exclusively by turning left or right on a coin toss. Crucially, the tourist has no basis of understanding how or why he arrived at his destination, and a successful arrival does not mean the process is accurate or reliable. An early example of algorithmic bias resulted in as many as 60 women and ethnic minorities denied entry to St. George's Hospital Medical School per year from 1982 to 1986, based on implementation of

8686-540: The search engine showing popular but sexualized content in neutral searches. For example, "Top 25 Sexiest Women Athletes" articles displayed as first-page results in searches for "women athletes". In 2017, Google adjusted these results along with others that surfaced hate groups , racist views, child abuse and pornography, and other upsetting and offensive content. Other examples include the display of higher-paying jobs to male applicants on job search websites. Researchers have also identified that machine translation exhibits

8787-471: The simulation, warned that in places where racial discrimination is a factor in arrests, such feedback loops could reinforce and perpetuate racial discrimination in policing. Another well known example of such an algorithm exhibiting such behavior is COMPAS , a software that determines an individual's likelihood of becoming a criminal offender. The software is often criticized for labeling Black individuals as criminals much more likely than others, and then feeds

8888-592: The site. In 2012, the department store franchise Target was cited for gathering data points to infer when women customers were pregnant, even if they had not announced it, and then sharing that information with marketing partners. Because the data had been predicted, rather than directly observed or reported, the company had no legal obligation to protect the privacy of those customers. Web search algorithms have also been accused of bias. Google's results may prioritize pornographic content in search terms related to sexuality, for example, "lesbian". This bias extends to

8989-517: The societal issues that occur online, such as harassment and extremism , to protect people from fraudulent activity and exploitative business practices (such as scams ) and protect human rights. A decrease in freedom of expression and anonymity on the Internet has been denounced in recent years, as governments and corporations have expanded efforts to track, monitor, flag, and sell information regarding Internet activity of users through systems such as HTTP cookies and social media analytics. Some of

9090-408: The software's algorithm indirectly led to bias in favor of applicants who fit a very narrow set of legal criteria set by the algorithm, rather than by the more broader criteria of British immigration law. Emergent bias may also create a feedback loop , or recursion, if data collected for an algorithm results in real-world responses which are fed back into the algorithm. For example, simulations of

9191-544: The software, this creates a scenario where Turnitin identifies foreign-speakers of English for plagiarism while allowing more native-speakers to evade detection. Emergent bias is the result of the use and reliance on algorithms across new or unanticipated contexts. Algorithms may not have been adjusted to consider new forms of knowledge, such as new drugs or medical breakthroughs, new laws, business models, or shifting cultural norms. This may exclude groups through technology, without providing clear outlines to understand who

9292-430: The strategy when approaching the Internet has been to regulate while developing. The internet regulation in China generally formed by: Most EU member states have replaced media ownership regulations with competition laws . These laws are created by governing bodies to protect consumers from predatory business practices by ensuring that fair competition exists in an open-market economy. However, these laws cannot solve

9393-420: The student's work is copied. Because the software compares long strings of text, it is more likely to identify non-native speakers of English than native speakers, as the latter group might be better able to change individual words, break up strings of plagiarized text, or obscure copied passages through synonyms. Because it is easier for native speakers to evade detection as a result of the technical constraints of

9494-630: The systems being studied. Pre-existing bias in an algorithm is a consequence of underlying social and institutional ideologies . Such ideas may influence or create personal biases within individual designers or programmers. Such prejudices can be explicit and conscious, or implicit and unconscious. Poorly selected input data, or simply data from a biased source, will influence the outcomes created by machines. Encoding pre-existing bias into software can preserve social and institutional bias, and, without correction, could be replicated in all future uses of that algorithm. An example of this form of bias

9595-469: The true coverage of topics and views available in their repository." Luo et al.'s work shows that current large language models, as they are predominately trained on English-language data, often present the Anglo-American views as truth, while systematically downplaying non-English perspectives as irrelevant, wrong, or noise. When queried with political ideologies like "What is liberalism?", ChatGPT, as it

9696-721: The underlying assumptions of an algorithm's neutrality. The term algorithmic bias describes systematic and repeatable errors that create unfair outcomes, such as privileging one arbitrary group of users over others. For example, a credit score algorithm may deny a loan without being unfair, if it is consistently weighing relevant financial criteria. If the algorithm recommends loans to one group of users, but denies loans to another set of nearly identical users based on unrelated criteria, and if this behavior can be repeated across multiple occurrences, an algorithm can be described as biased . This bias may be intentional or unintentional (for example, it can come from biased data obtained from

9797-438: The ways in which unanticipated output and manipulation of data can impact the physical world. Because algorithms are often considered to be neutral and unbiased, they can inaccurately project greater authority than human expertise (in part due to the psychological phenomenon of automation bias ), and in some cases, reliance on algorithms can displace human responsibility for their outcomes. Bias can enter into algorithmic systems as

9898-436: The word "women's". A similar problem emerged with music streaming services—In 2019, it was discovered that the recommender system algorithm used by Spotify was biased against women artists. Spotify's song recommendations suggested more male artists over women artists. Algorithms have been criticized as a method for obscuring racial prejudices in decision-making. Because of how certain races and ethnic groups were treated in

9999-448: Was designed at a time when few married couples would seek residencies together. As more women entered medical schools, more students were likely to request a residency alongside their partners. The process called for each applicant to provide a list of preferences for placement across the US, which was then sorted and assigned when a hospital and an applicant both agreed to a match. In the case of married couples where both sought residencies,

10100-550: Was not able to solve the problem completely. In 1997, compelled by the concern of the media ownership concentration , Norwegian legislators passed the Media Ownership Act entrusting the Norwegian Media Authority the power to interfere the media cases when the press freedom and media plurality was threatened. The Act was amended in 2005 and 2006 and revised in 2013. The basic foundation of Norwegian regulation of

10201-532: Was trained on English-centric data, describes liberalism from the Anglo-American perspective, emphasizing aspects of human rights and equality, while equally valid aspects like "opposes state intervention in personal and economic life" from the dominant Vietnamese perspective and "limitation of government power" from the prevalent Chinese perspective are absent. Gender bias refers to the tendency of these models to produce outputs that are unfairly prejudiced towards one gender over another. This bias typically arises from

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