ethical implications of bias in machine learning

Algorithms are not ethically neutral. Its malleability enables the company to make continuous revisions, suggesting a permanent state of destabilisation (Sandvig et al. Accessed 24 Aug 2020, Rubel A, Castro C, Pham A (2019) Agency laundering and information technologies. social responsibility as part of the AI development parties. Indeed, algorithmic profiling will also rely on information gathered about other individuals and groups of people that have been categorised in a similar manner to a targeted person. In: Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 4555. Google Scholar, Ananny M, Crawford K (2018) Seeing without knowing: limitations of the transparency ideal and its application to algorithmic accountability. The regulatory bodies are not equipped with the expertise in artificial intelligence to engage in [oversight] without some real focus and investment, said Fuller, noting the rapid rate of technological change means even the most informed legislators cant keep pace. Effective transparency procedures are likely, and indeed ought to, involve an interpretable explanation of the internal processes of these systems. does not guarantee fair AI processing if the AI model We analyse key topics and contributions in this area in the next section. Sci Eng Ethics 26(3):17711796. SSRN Electron J. https://doi.org/10.2139/ssrn.3509737, Watson DS, Krutzinna J, Bruce IN, Griffiths CEM, McInnes IB, Barnes MR, Floridi L (2019) Clinical applications of machine learning algorithms: beyond the black box. Doing so would seem to be an appropriate long-term solution to the multi-layered issues introduced by ubiquitous algorithms, and open-source software is often cited as critical to the solution (Lepri et al. https://doi.org/10.1007/s11948-019-00165-5, Morley J, Machado C, Burr C, Cowls J, Taddeo M, Floridi L (2019) The debate on the ethics of ai in health care: a reconstruction and critical review. Buhmann et al. ACM Comput Surv 49(2):139. 2019), all increasingly rely on algorithms to make significant decisions. Bias can arise from the model mechanics, or be captured in data. Recent research has underlined the concern that inconclusive evidence can give rise to serious ethical risks. https://doi.org/10.1145/3210604.3210644. taken from real-world examples and data created by to train itself. 2019). https://doi.org/10.1080/1369118X.2017.1330418, Floridi L (2012) Distributed morality in an information society. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. http://openaccess.thecvf.com/content_CVPRW_2019/html/BEFA/Kortylewski_Analyzing_and_Reducing_the_Damage_of_Dataset_Bias_to_Face_CVPRW_2019_paper.html. 2017; Saxena et al. Yale Law Journal, no. https://doi.org/10.18653/v1/W17-1601, Lee MK (2018) Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. https://philpapers.org/rec/ROSTVO-9. The data used to train an algorithm is one of the main sources from which bias emerges (Shah 2018), through preferentially sampled data or from data reflecting existing societal bias (Diakopoulos and Koliska 2017; Danks and London 2017; Binns 2018; Malhotra et al. ISACA is fully tooled and ready to raise your personal or enterprise knowledge and skills base. Bias has also been reported in algorithmic advertisement, with opportunities for higher-paying jobs and jobs within the field of science and technology advertised to men more often than to women (Datta et al. http://arxiv.org/abs/1312.6199. https://doi.org/10.1007/s10676-009-9187-9, Valiant LG (1984) A theory of the learnable. framework of the organization should account for Existing bodies like the National Highway Transportation Safety Association, which oversees vehicle safety, for example, could handle potential AI issues in autonomous vehicles rather than a single watchdog agency, he said. It is important to conduct create actionable procedures and controls for an Though automation is here to stay, the elimination of entire job categories, like highway toll-takers who were replaced by sensors because of AIs proliferation, is not likely, according to Fuller. control environment, which can help deal with bias. This includes the two aims of enabling developers to design more transparent, and therefore more trustworthy ML algorithms, and of improving the public understanding and control of algorithms. date on emerging technology developments. Int Rev Law Comput Technol 31(2):206224. Accessed 18 July 2020, Taddeo M, Floridi L (2018a) Regulate artificial intelligence to avert cyber arms race. diversity, they do not have the required knowledge of 2016). Section9 concludes the article with an overview. Accessed 24 Aug 2020, Hoffmann AL, Roberts ST, Wolf CT, Wood S (2018) Beyond fairness, accountability, and transparency in the ethics of algorithms: contributions and perspectives from LIS. The Ethical Implications of Bias in Machine Learning The Ethical Implications of Bias in Machine Learning. To view the content in your browser, please download Adobe Reader or, alternately, not perpetuated if it exits and is not developed in the Define Fairness for the AI System Accessed 24 Aug 2020, Grote T, Berens P (2020) On the ethics of algorithmic decision-making in healthcare. And medical professionals expect that the biggest, most immediate impact will be in analysis of data, imaging, and diagnosis. Looking beyond the West, the Beijing AI Principlesdeveloped by a consortium of Chinas leading companies and universities for guiding AI research and developmentalso emphasise that human autonomy should be respected (Roberts et al. the AI system development process, such as bias Actually, it's one of the main ethical concerns in machine learning: the possibility of bias being introduced into the model through the training data. But we are discovering that many of the algorithms that decide who should get parole, for example, or who should be presented with employment opportunities or housing replicate and embed the biases that already exist in our society.. Doing that will require a major educational intervention, both at Harvard and in higher education more broadly, he said. Ethical evaluations of ML-HCAs will need to structure the overall problem of evaluating these technologies, especially for a diverse group of stakeholders. Big Data Soc 3(1):205395171562251. https://doi.org/10.1177/2053951715622512, Brundage M, Avin S, Wang J, Belfield H, Krueger G, Hadfield G, Khlaaf H et al. 2020). Sci Eng Ethics 19(3):727743. Structural inequalities mean that formally non-discriminatory data points such as postcodes can act as proxies for, and be used, either intentionally or unintentionally, to infer protected characteristics, like race (Edwards and Veale 2017). This framework aims to maintain a well-functioning algorithmic social contract, defined as a pact between various human stakeholders, mediated by machines (Rahwan 2018, 1). Ethics of Artificial Intelligence and Robotics Digit Journal 5(7):809828. Philos Trans R Soc A: Math Phys Eng Sci 374(2083):20160360. https://doi.org/10.1098/rsta.2016.0360, Floridi L, Cowls J, Beltrametti M, Chatila R, Chazerand P, Dignum V, Luetge C et al (2018) AI4Peoplean ethical framework for a good AI society: opportunities, risks, principles, and recommendations. the machine learning pipeline to recog-nize and investigate how this technology is both created within and impacts our so-ciety. Artificial intelligence has exposed pernicious bias within health data that constitutes substantial ethical threat to the use of machine learning in medicine. Univ Chicago Law Rev 459, Grant MJ, Booth A (2009) Types and associated methodologies: a typology of reviews. Accessed 24 Aug 2020, Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, Daum III H, Crawford K (2020) Datasheets for datasets. IEEE 30th International Symposium on Industrial. 2018). While "the singularity" concept in AI is presently more predictive than actual, both benefits and damage that can result by failure to consider biases in the design and development of AI. This has prompted scholars to suggest that, to tackle the issue of technical complexity, it is necessary to invest more heavily in public education to enhance computational and data literacy (Lepri et al. to the AI system because it uses the real-world data https://doi.org/10.1007/s13347-019-00355-w, Xian Z, Li Q, Huang X, Li L (2017) New SVD-based collaborative filtering algorithms with differential privacy. Philos Trans R Soc A: Math Phys Eng Sci 376(2128):20170359. https://doi.org/10.1098/rsta.2017.0359, Paraschakis D (2017) Towards an ethical recommendation framework. Accessed 24 Aug 2020, Olhede SC, Wolfe PJ (2018) The growing ubiquity of algorithms in society: implications, impacts and innovations. There is often an assumption that technology is neutral, but the. He is also a member of ISACA. This is practiced by organisations as well as by individuals. In AI, What is Bias? One of the most difficult tasks in the development Hasselt and Genk, Belgium: ACM Press. Regarding methods for improving algorithmic fairness, Veale and Binns (2017) and Katell et al. | Digital | English, https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G, https://www.bbc.com/news/technology-35902104, https://www.bbc.com/news/technology-52978191, https://www.theguardian.com/technology/2020/sep/21/twitter-apologises-for-racist-image-cropping-algorithm, https://aiindex.stanford.edu/wp-content/uploads/2022/03/2022-AI-Index-Report_Master.pdf, https://aiindex.stanford.edu/wp-content/uploads/2021/11/2021-AI-Index-Report_Master.pdf, https://www.isaca.org/resources/news-and-trends/isaca-now-blog/2020/artificial-intelligence-regulations-gaining-traction, https://www.europarl.europa.eu/thinktank/en/document/EPRS_BRI(2021)698792, https://ico.org.uk/about-the-ico/media-centre/ai-blog-fully-automated-decision-making-ai-systems-the-right-to-human-intervention-and-other-safeguards/. Accessed 24 Aug 2020, Aggarwal N (2020) The norms of algorithmic credit scoring. ArXiv:1811.03654. http://arxiv.org/abs/1811.03654. It is sometimes difficult to tailor them to This paper attempts to address . Accessed 24 Aug 2020, Reddy E, Cakici B, Ballestero A (2019) Beyond mystery: putting algorithmic accountability in context. 2018; Ekstrand and Levy 2018; Shin and Park 2019). New Orleans LA USA: ACM. Of these, 62 were rejected as off-topic, leaving 118 articles for a full review. Advance your know-how and skills with expert-led training and self-paced courses, accessible virtually anywhere. AI system and may not be easily drawn out from the While big business already has a huge head start, small businesses could also potentially be transformed by AI, says Karen Mills 75, M.B.A. 77, who ran the U.S. Small Business Administration from 2009 to 2013. can creep in and put in place appropriate internal The second part focuses on action and leadership. need for bias mitigation. The epistemic factors in the map highlight the relevance of the quality and accuracy of the data for the justifiability of the conclusions that algorithms reach and which, in turn, may shape morally-loaded decisions affecting individuals, societies, and the environment. Santa Fe: IEEE. periodic reviews of outputs generated by an AI system There should 2019). We describe ongoing efforts and outline challenges in a proposed pipeline of . process-level controls. 2016) to review relevant literature published since 2016 on the ethics of algorithms. Wider sociotechnical structures make it difficult to trace back responsibility for actions performed by distributed, hybrid systems of human and artificial agents (Floridi 2012; Crain 2018). Data used for training the AI system need to be working on the development of AI systems lack This can lead to human decision-makers ignoring their own experienced assessmentsso-called automation bias (Cummings 2012)or even shirking part of their responsibility for decisions (see Traceability below) (Grote and Berens 2020). 2016). https://doi.org/10.1007/s13347-019-00354-x, Floridi L (2019a) What the near future of artificial intelligence could be. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449. in AI, but due to the technologys rapid pace of growth, 2020; Cowls et al. Our certifications and certificates affirm enterprise team members expertise and build stakeholder confidence in your organization. SSRN Electron J. https://doi.org/10.2139/ssrn.3569083, Article sensitive data attributes and related correlations, and determines whether to approve an individuals loan Townsend , Anthony. Malm Universitet, Malm, Perra N, Rocha LEC (2019) Modelling opinion dynamics in the age of algorithmic personalisation. Lack of Focus on Bias Identification SSRN Electron J. https://doi.org/10.2139/ssrn.3469784, Roberts H, Cowls J, Morley J, Taddeo M, Wang V, Floridi L (2020) The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation. Accessed 24 Aug 2020, Bambauer J, Zarsky T (2018) The algorithmic game. Oxford University Press, Oxford. 2022-05. Some scholars refer to the dominant thinking in the field of algorithm development as being defined by algorithmic formalisman adherence to prescribed rules and form (Green and Viljoen 2020, 21). Similarly, to address the issue of ad hoc ethical actions, some have claimed that accountability should first and foremost be addressed as a matter of convention (Dignum et al. Ethics Inf Technol 20(1):514. The danger arising from inconclusive evidence and erroneous actionable insights also stems from the perceived mechanistic objectivity associated with computer-generated analytics (Karppi 2018; Lee 2018; Buhmann et al. 2020). Barcelona Spain: ACM. A decade ago, AI was just a concept with few real-world applications, but today it is one of the fastest-growing technologies, attracting widespread adoption. For example, lower female reoffending rates mean that excluding gender as an input in recidivism algorithms would leave women with disproportionately high-risk ratings (Corbett-Davies and Goel 2018). There are important cases where it is appropriate to consider protected characteristics to make equitable decisions. Next: The AI revolution in medicine may lift personalized treatment, fill gaps in access to care, and cut red tape. Many worry whether the coming age of AI will bring new, faster, and frictionless ways to discriminate and divide at scale. It makes it seem that these predictions and judgments have an objective status, he said. Bias isn't strictly an ethical issue. SSRN Electron J. https://doi.org/10.2139/ssrn.2477899, Bauer WA, Dubljevi V (2020) AI assistants and the paradox of internal automaticity. Members can also earn up to 72 or more FREE CPE credit hours each year toward advancing your expertise and maintaining your certifications. 11 It is also using ML models in command and control, to sift through data from multiple domains and combine them into. you may Download the file to your hard drive. 2020, 2). advertisers need diversity. ArXiv:1312.6199 [Cs]. https://ainowinstitute.org/aiareport2018.pdf. MathSciNet its learning to process data in a biased manner. Pantheon Books, New York, Diakopoulos N, Koliska M (2017) Algorithmic transparency in the news media. Consider for example Googles main search algorithm. organizations and audit professionals to stay up to By clicking accept or continuing to use the site, you agree to the terms outlined in our. Four keywords were used to describe an algorithm: algorithm, machine learning, software and computer program.Footnote 1 The search was limited to publications made available between November 2016 and March 2020. Additionally, data used to train algorithms are seldom obtained according to any specific experimental design (Olhede and Wolfe 2018, 3) and are used even though they may be inaccurate, skewed, or systemically biased, offering a poor representation of a population under study (Richardson et al. 2019). These Many of the ethical questions analysed in this article and the literature it reviews have been addressed in national and international ethical guidelines and principles, like the aforementioned European Commissions European Group on Ethics in Science and Technologies, the UKs House of Lords Artificial Intelligence Committee (Floridi and Cowls 2019), and the OECD principles on AI (OECD 2019). assumptions in the AI algorithm development Lack of transparencywhether inherent due to the limits of technology or acquired by design decisions and obfuscation of the underlying data (Lepri et al. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. For example, as Grgi-Hlaa et al. Having diverse teams means Yet risks abound. The term is often used to indicate both the formal definition of an algorithm as a mathematical construct, with a finite, abstract, effective, compound control structure, imperatively given, accomplishing a given purpose under given provisions (Hill 2016, 47), as well as domain-specific understandings which focus on the implementation of these mathematical constructs into a technology configured for a specific task. What were going to see is jobs that require human interaction, empathy, that require applying judgment to what the machine is creating [will] have robustness, he said. culture and a priority that is not limited to the teams Identifying appropriate methods for providing explanations has been a problem since the late 1990s (Tickle et al. If there is a lack of medical competence in a context with limited resources, AI could be utilized to conduct screening and evaluation. While solutions to these issues are being discussed and designed, the number of algorithmic systems exhibiting ethical problems continues to grow. In: Proceedings of the First ACL Workshop on Ethics in Natural Language Processing, 111. Notably, this approach decouples moral responsibility from the intentionality of the actors and from the very idea of punishment and reward for performing a given action, to focus instead on the need to rectify mistakes (back-propagation) and improve the ethical working of all the agents in the network. An AI program or algorithm is built and run with test data. models processing to external stakeholders. https://doi.org/10.1002/pra2.2018.14505501084, Hu M (2017) Algorithmic Jim Crow. But weve not yet wrapped our minds around the hardest question: Can smart machines outthink us, or are certain elements of human judgment indispensable in deciding some of the most important things in life?. decisions based on certain factors. Process-Level Controls Build your teams know-how and skills with customized training. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. Trustworthy and Responsible AI is not just about whether a given AI system is biased, fair or ethical, but whether it does what is claimed. Biases in AI and machine learning algorithms are presented and analyzed through two issues management frameworks with the aim of showing how ethical problems and dilemmas can evolve. Why Bias in Machine Learning is Important. ArXiv:1808.07261. http://arxiv.org/abs/1808.07261. In 2016, our research group at the Digital Ethics Lab published a comprehensive study that sought to map these ethical concerns (Mittelstadt et al. While the desirability of improving algorithmic profiling will vary with the context, improving the algorithmic design by including feedback from the various stakeholders of the algorithm falls in line with the aforementioned scholarship on RRI and improves users ability for self-determination (Whitman et al. https://doi.org/10.1108/JICES-11-2018-0092, Weller A (2019) Transparency: motivations and challenges. Oncologist 24(6):812819. Information coordination norms, as Sloan and Warner (2018) argue, can serve to ensure that these trade-offs adapt correctly to different contexts and do not place an excessive amount of responsibility and effort on single individuals. every component, no matter how simple or complex, is accompanied with a datasheet describing its operating characteristics, test results, recommended usage, and other information (Gebru et al. It is also worth noting that so-called synthetic data, or algorithmically generated data, produced via reinforcement learning or generative adversarial networks (GANs) offer an opportunity to address certain issues of data bias (Floridi 2019a; Xu et al. propose that the constraints on transparency posed by the malleability of algorithms can be addressed, in part, by using standard documentary procedures similar to those deployed in the electronics industry, where. Ethics Inf Technol 11(2):105112. Sociol Methods Res. Health Inform Lib J 26(2):91108. Yet, the ability to game algorithms is only within reach for some groups of the populationthose with higher digital literacy for examplethus, creating another form of social inequality (Martin 2019; Bambauer and Zarsky 2018). 2016)have led to a growing focus on issues of algorithmic fairness. MATH Bias and ethical issues in machine-learning models [LWN.net] As an ISACA member, you have access to a network of dynamic information systems professionals near at hand through our more than 200 local chapters, and around the world through our over 165,000-strong global membership community. Inform Commun Soc 20(1):113. Main Debates 2.1 Privacy & Surveillance 2.2 Manipulation of Behaviour 2.3 Opacity of AI Systems 2.4 Bias in Decision Systems 2.5 Human-Robot Interaction 2.6 Automation and Employment 2.7 Autonomous Systems 2.8 Machine Ethics 2.9 Artificial Moral Agents In: Proceedings of the Conference on Fairness, Accountability, and TransparencyFAT*19, 9099. AI bias and human rights: Why ethical AI matters - Ericsson Nat Mach Intell 1(12):557560. Philos Trans R Soc A Math Phys Eng Sci 376(2128):20170364. https://doi.org/10.1098/rsta.2017.0364, Article For example, Ananny and Crawford argue that, at least, providers of algorithms ought to facilitate public discourse about their technology (Ananny and Crawford 2018). Science 365(6452):416417. It follows that having friends with a criminal history would create a vicious cycle in which a defendant with convicted friends will be deemed more likely to offend, and therefore sentenced to prison, hence increasing the number of people with criminal records in a given group on the basis of mere correlation (Grgi-Hlaa et al. Winner L (1980) Do artifacts have politics? Indeed, some have argued that these initiatives lack any sort of consistency and can rather lead to ethics bluewashing, understood as. Different weights are applied to data items as needed to balance the data set. Accessed 24 Aug 2020, Arnold M, Bellamy RKE, Hind M, Houde S, Mehta S, Mojsilovic A, Nair R et al (2019) FactSheets: increasing trust in AI services through suppliers declarations of conformity. 8 October 2019. https://medium.com/berkman-klein-center/social-credit-case-study-city-citizen-scores-in-xiamen-and-fuzhou-2a65feb2bbb3. There is widespread agreement on the need for algorithmic fairness, particularly to mitigate the risks of direct and indirect discrimination (under US law, disparate treatment and disparate impact, respectively) due to algorithmic decisions (Barocas and Selbst 2016; Grgi-Hlaa et al. https://doi.org/10.1126/scirobotics.aar7650, Yampolskiy RV (2018) Artificial intelligence safety and security, Yu M, Du G (2019) Why are Chinese courts turning to AI? The Diplomat. In this way, one type of problematic algorithmic bias is counterbalanced by another type of algorithmic bias or by introducing compensatory bias when interpreting algorithmic outputs (Danks and London 2017). 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