Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor


Covered by SCOPUS


CiteScore 2018: 2.17

SCImago Journal Rank (SJR) 2018: 0.336
Source Normalized Impact per Paper (SNIP) 2018: 1.707

ICV 2017: 99.90

Open Access
Online
ISSN
2081-4836
See all formats and pricing
More options …

AI for the Common Good?! Pitfalls, challenges, and ethics pen-testing

Bettina Berendt
Published Online: 2019-01-11 | DOI: https://doi.org/10.1515/pjbr-2019-0004

Abstract

Recently, many AI researchers and practitioners have embarked on research visions that involve doing AI for “Good”. This is part of a general drive towards infusing AI research and practice with ethical thinking. One frequent theme in current ethical guidelines is the requirement that AI be good for all, or: contribute to the Common Good. Butwhat is the Common Good, and is it enough to want to be good? Via four lead questions, I will illustrate challenges and pitfallswhen determining, from an AI point of view,what the Common Good is and how it can be enhanced by AI. The questions are: What is the problem / What is a problem?, Who defines the problem?, What is the role of knowledge?, and What are important side effects and dynamics? The illustration will use an example from the domain of “AI for Social Good”, more specifically “Data Science for Social Good”. Even if the importance of these questions may be known at an abstract level, they do not get asked sufficiently in practice, as shown by an exploratory study of 99 contributions to recent conferences in the field. Turning these challenges and pitfalls into a positive recommendation, as a conclusion I will draw on another characteristic of computer-science thinking and practice to make these impediments visible and attenuate them: “attacks” as a method for improving design. This results in the proposal of ethics pen-testing as a method for helping AI designs to better contribute to the Common Good.

Keywords: artificial intelligence; machine learning; data science; AI ethics; ethics and ethics codes; risks and impacts; Common Good; AI for Social Good

References

  • [1] L. Pangrazio, Exploring provocation as a research method in the social sciences, International Journal of Social Research Methodology, 2017, 20(2), 225Google Scholar

  • [2] D. Boyd, K. Crawford, Critical questions for big data, Information, Communication & Society, 2012, 15(5), 662-679CrossrefGoogle Scholar

  • [3] ACM Code of Ethics and Professional Conduct, 1992, https://www.acm.org/about-acm/acm-code-of-ethics-andprofessional-conductGoogle Scholar

  • [4] Asilomar AI Principles, Future of Life Institute, 2017, https://futureoflife.org/ai-principles/Google Scholar

  • [5] The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, Ethically Aligned Design: A Vision for Prioritizing Human Wellbeing with Artificial Intelligence and Autonomous Systems, Version 1 for public discussion, 2016, http://standards.ieee.org/develop/indconn/ec/ead_v1.pdfGoogle Scholar

  • [6] S. Lee, Common Good, In: Encyclopedia Britannica (n.d.), https://www.britannica.com/topic/common-goodGoogle Scholar

  • [7] W. Hussain, The Common Good, In: E. N. Zalta (Ed.), The Stanford Encycopledia of Philosophy, Spring 2018 edition, https://plato.stanford.edu/archives/spr2018/entries/common-good/Google Scholar

  • [8] C. Blum, Determining the Common Good: A (re-)constructive critique of the proceduralist paradigm, Phenomenology and Mind, 2012, 3, 176-188Google Scholar

  • [9] K. Popper, The Open Society and Its Enemies , Routledge, UK, 1945Google Scholar

  • [10] M. Jaede, The concept of the Common Good, Working Paper Series of the Political Settlements Research Programme (PSRP) of the University of Edinburgh, Edinburgh, UK, 2017, https://www.britac.ac.uk/sites/default/files/Jaede.pdfGoogle Scholar

  • [11] J. Cohen, Procedure and substance in deliberative democracy, In: J. Bohman, W. Rehg (Eds.), Deliberative Democracy: Essays on Reason and Politics , MIT Press, Boston, MA, 1997, 407-437Google Scholar

  • [12] G. Capoccia, Militant democracy: The institutional bases of democratic self-preservation, Annual Review of Law and Social Science, 2013, 9(1), 207-226CrossrefGoogle Scholar

  • [13] R. De Wolf, E. Vanderhoven, B. Berendt, J. Pierson, T. Schellens, Self-reflection on privacy research in social networking sites, Behaviour & Information Technology, 2017, 36(5), 459-469CrossrefGoogle Scholar

  • [14] World Economic Forum, Artificial Intelligence for the Common Good, Sustainable, Inclusive and Trustworthy, 2018, https://weforum.ent.box.com/v/AI4GoodGoogle Scholar

  • [15] North Highland Consulting, AI for the Common Good, An Ethical Framework to Harness AI’s Greatest Potential, 2018, http://www.northhighland.com/insights/white-papers/ai-forthe-common-goodGoogle Scholar

  • [16] B. Mols, AI for the Common Good, ACM News, Jun. 20, 2017, https://cacm.acm.org/news/218696-ai-for-the-commongood/fulltextGoogle Scholar

  • [17] A. Tanweer, B. Fiore-Gartland, Cross-sector collaboration in Data Science for Social Good: Opportunities, challenges, and open questions raised by working with academic researchers, In: Data Science for Social Good Conference, Sep. 28-29, 2017, Chicago, IL, http://dssg.uchicago.edu/wp-content/uploads/2017/09/tanweer.pdfGoogle Scholar

  • [18] B. J. Copeland, Artificial intelligence, In: Encyclopedia Britannica (n.d.), https://www.britannica.com/technology/artificialintelligenceGoogle Scholar

  • [19] L. Cao, Data Science: A comprehensive overview, ACM Computing Surveys, 2017, 50(3), Article No. 43Google Scholar

  • [20] D. Conway, The Data Science Venn Diagram (n.d.), http://drewconway.com/zia/2013/3/26/the-data-sciencevenn- diagramGoogle Scholar

  • [21] Wikibooks, The Free Textbook Project, Cognitive Psychology and Cognitive Neuroscience/Knowledge Representation and Hemispheric Specialisation, 2017, https://en.wikibooks.org/w/index.php?title=Cognitive_Psychology_and_Cognitive_Neuroscience/Knowledge_Representation_and_Hemispheric_Specialisation&oldid=3277633Google Scholar

  • [22] S. J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Englewood Cliffs, NJ, 1995Google Scholar

  • [23] U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, Knowledge discovery and data mining: towards a unifying framework, In: KDD’96 Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996, 82-88Google Scholar

  • [24] G. Veruggio, Roboethics [TC Spotlight], IEEE Robotics&Automation Magazine, 2010, 17(2), 105-109CrossrefGoogle Scholar

  • [25] P. M. Asaro, What should we want from a robot ethic? International Review of Information Ethics, 2006, 6, 9-16Google Scholar

  • [26] B. F. Malle, Integrating robot ethics and machine morality: The study and design of moral competence in robots, Ethics and Information Technology, 2016, 18(4), 243-256Google Scholar

  • [27] W. Wallach, C. Allen, Moral machines: Teaching robots right from wrong, Oxford University Press, New York, NY, 2008Google Scholar

  • [28] S. A. Fricker, R. Grau, A. Zwingli, Requirements engineering: Best practice, In: S. A., Fricker, C. Thummler, A. Gavras (Eds.), Requirements Engineering for Digital Health, Springer, USA, 2015, 25-46Google Scholar

  • [29] AOIR (Association of Internet Researchers), Ethical Decision- Making and Internet Research: Recommendations from the AoIR Ethics Working Committee (Version 2.0), 2012, https://aoir.org/reports/ethics2.pdfGoogle Scholar

  • [30] J. Goguen, Requirements engineering as the reconciliation of technical and social issues, In: Requirements Engineering: Social and Technical Issues, Academic Press, 1994, 165-199Google Scholar

  • [31] S. M. Bird, A. McAuley, S. Perry, C. Hunter, Effectiveness of Scotland’s National Naloxone Programme for reducing opioidrelated deaths: a before (2006-10) versus after (2011-13) comparison, Addiction, 2016, 111(5), 883-891Google Scholar

  • [32] I. Amrani, Here in Colombia, the hypocrisy of western cocaine users is laid bare, The Guardian, Aug. 1, 2018, https://www.theguardian.com/commentisfree/2018/aug/01/colombiahypocris-western-middle-class-cocaine-users-violenceGoogle Scholar

  • [33] A. Kovacevic, Engineering Design Process - Part 1, Problem Definition, 2017, http://www.staff.city.ac.uk/~ra600/ME1105/Lectures/ME1110-11.pdfGoogle Scholar

  • [34] P. Naur, Formalization in program development, BIT Numerical Mathematics, 1982, 22(4), 437-453CrossrefGoogle Scholar

  • [35] F. Villamor, Death of Philippine teenager stokes opposition to Duterte’s drug crackdown, The New York Times, Aug. 23, 2017, https://www.nytimes.com/2017/08/23/world/asia/duterte-drug-crackdown.html?mcubz=3Google Scholar

  • [36] ACLU, Written Submission of the American Civil Liberties Union on Racial Disparities in Sentencing, Hearing on Reports of Racism in the Justice System of the United States submitted to the Inter-American Commission on Human Rights, 2014, https://www.aclu.org/sites/default/files/assets/141027_iachr_racial_disparities_aclu_submission_0.pdfGoogle Scholar

  • [37] K. Gwynne, 4 biggest myths about crack, Salon, Oct. 8, 2013, https://www.salon.com/2013/08/10/busting_the_crack_propaganda_myths_partner/Google Scholar

  • [38] J. Mechanic, When a drug epidemic hit white America, addiction became a disease, Huflngton Post, Jul. 10, 2017, https://www.huflngtonpost.com/entry/when-a-drug-epidemic-hit-whiteamerica-addiction-became_us_5963a588e4b08f5c97d06b9aGoogle Scholar

  • [39] K. Lum, W. Isaac, To predict and serve?, Significance, 2016, 13(5), 14-19Google Scholar

  • [40] A. Chouldechova, Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, Big Data, 2017, 5(2), 153-163Google Scholar

  • [41] J. Kleinberg, S. Mullainathan, M. Raghavan, Inherent trade-offs in the fair determination of risk scores, In: Proceedings of the 8th Conference on Innovations in Theoretical Computer Science (ITCS), 2017Google Scholar

  • [42] D. Tewksbury, D. A. Scheufele, News framing theory and research, In: J. Bryant, M. B.Oliver (Eds.), Media effects: Advances in theory and research, Earlbaum, Hillsdale, NJ, 2009, 17-33Google Scholar

  • [43] D. Baum, Legalize it all, How to win the war on drugs, Harper’s Magazine, Apr. 4, 2016, https://harpers.org/archive/2016/04/legalize-it-all/Google Scholar

  • [44] Wikipedia contributors, David Nutt, In: Wikipedia, The Free Encyclopedia, 2018, https://en.wikipedia.org/w/index.php?title=David_Nutt&oldid=852024763Google Scholar

  • [45] S. Ewen, PR! A Social History of Spin, Basic Books, New York, 1996Google Scholar

  • [46] E. Bakshy, S. Messing, L. A. Adamic, Exposure to ideologically diverse news and opinion on Facebook, Science, 2015, 348(6239), 1130-1132Google Scholar

  • [47] M. Del Vicario, G. Vivaldo, A. Bessi, F. Zollo, A. Scala, G. Caldarelli, W. Quattrociocchi, Echo chambers: Emotional contagion and group polarization on Facebook, Scientific Reports, 2016, 6, Article 37825Google Scholar

  • [48] H. T. P. Williams, J. R. McMurray, T. Kurz, F. H. Lambert, Network analysis reveals open forums and echo chambers in social media discussions of climate change, Global Environmental Change, 2015, 32, 126-138Google Scholar

  • [49] T. T. Nguyen, P.-M. Hui, F. M. Harper, L. Terveen, J. A. Konstan, J. A., Exploring the filter bubble: The effect of using recommender systems on content diversity, In: Proceedings of International World Wide Web Conference Committee (WWW’14), ACM, New York, 2014Google Scholar

  • [50] L. Taylor, The ethics of big data as a public good: which public? Whose good? Philosophical Transactions, Series A, Mathematical, Physical, and Engineering Sciences, 2016, 374(2083), 20160126Google Scholar

  • [51] L. Taylor, Safety in numbers? Group privacy and big data analytics in the developing world, In: L. Taylor, B. van der Sloot, L. Floridi (Eds.), Group Privacy: the Challenges of New Data Technologies, Springer, Berlin, 2017Google Scholar

  • [52] C. Barabas, K. Dinakar, J. Ito, M. Virza, J. Zittrain, Interventions over predictions: Reframing the ethical debate for actuarial risk assessment, Proceedings of Machine Learning Research, 2018, 81, 1-15, http://proceedings.mlr.press/v81/barabas18a/barabas18a.pdfGoogle Scholar

  • [53] M. Kunaver, T. Požrl, Diversity in recommender systems - a survey, Knowledge-Based Systems, 2017, 123, 154-162Google Scholar

  • [54] B. Berendt, B. Gao, S. Gürses, T. Peetz, J. Pierson, SPION Deliverable 5.2 - Report on Research Activities (Feedback and Awareness Tools), COSIC Technical Report, KU Leuven, Leuven, Belgium, 2014, https://www.cosic.esat.kuleuven.be/publications/article-2496.pdfGoogle Scholar

  • [55] A. Jameson, B. Berendt, S. Gabrielli, F. Cena, C. Gena, F. Vernero, K. Reinecke, Choice architecture for human-computer interaction, Foundations and Trends in Human-Computer Interaction, 2013, 7(1-2), 1-235Google Scholar

  • [56] R. Shamir, The age of responsibilization: on market-embedded morality, Economy and Society, 2008, 37(1), 1-19CrossrefGoogle Scholar

  • [57] G. Rockwell, B. Berendt, Information wants to be free, or does it? The ethics of datafication, Electronic Book Review, 2017, http://electronicbookreview.com/thread/technocapitalism/datafictionGoogle Scholar

  • [58] M. Gross, L. McGoey (Eds.), Routledge International Handbook of Ignorance Studies, Routledge, London / New York, 2015Google Scholar

  • [59] A. Yakushev, S. Mityagin, Social networks mining for analysis and modeling drugs usage, In: Proceedings of the 14th International Conference on Computational Science (ICCS 2014), Procedia Computer Science, 2014, 29, 2462-2471Google Scholar

  • [60] M. Kosinski, D. Stillwell, T. Graepel, Private traits and attributes are predictable from digital records of human behavior, PNAS, 2013, 110(15), 5802-5805Google Scholar

  • [61] P. Greenfield, The Cambridge Analytica files: The story so far, The Guardian, Mar. 26, 2018, https://www.theguardian.com/news/2018/mar/26/the-cambridge-analytica-files-the-storyso-farGoogle Scholar

  • [62] S. Barocas, A. D. Selbst, Big data’s disparate impact, 104 California Law Review 671, 2016, http://dx.doi.org/10.2139/ssrn.2477899CrossrefGoogle Scholar

  • [63] C. O’Neil, Weapons ofMath Destruction, Crown Publishers, New York, 2016Google Scholar

  • [64] B. Berendt, S. Preibusch, Toward accountable discriminationaware data mining: The importance of keeping the human in the loop - and under the looking-glass, Big Data, 2017, 5(2), 135-152Google Scholar

  • [65] D. Ensign, S. A. Friedler, S. Neville, C. Scheidegger, S. Venkatasubramanian, Runaway feedback loops in predictive policing, In: Proceedings of Machine Learning Research, 2018, 81, http: //proceedings.mlr.press/v81/ensign18a/ensign18a.pdfGoogle Scholar

  • [66] E. Morozov, To Save Everything, Click Here: The Folly of Technological Solutionism, Public Affairs, New York, 2013Google Scholar

  • [67] R. Gavaldà, I. Koprinska, S. Kramer (Eds.), Proceedings of the Second Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, Sep. 18, 2017, CEUR Workshop Proceedings Vol-1960, http://ceur-ws.org/Vol-1960/Google Scholar

  • [68] E. Horvitz, B. Grosz, A. Greenwald, D. Parkes, C. Gomes, S. Smith, et al. (Eds.), Artificial Intelligence for the Social Good, Papers from the 2017 AAAI Spring Symposium, Technical Report SS-17-01, AAAI, Palo Alto, CA, 2017, https://www.aaai.org/Library/Symposia/Spring/ss17-01.phpGoogle Scholar

  • [69] J. Garriga, J. Piera, F. Bartumeus, A Bayesian framework for reputation in citizen science, In: Proceedings of the Second Workshop on Data Science for Social Good, CEUR Workshop Proceedings, 2017, 1960, 1-18, http://ceur-ws.org/Vol-1960/paper6.pdfGoogle Scholar

  • [70] F. Bria, The role of cities in democratizing AI and data ownership: Learning from Barcelona, Presentation at The AI for Good Global Summit 2018, Geneva, May 15-17, 2018, https://www.itu.int/en/ITU-T/AI/2018/Documents/Presentations/Francesca%20Bria.pdfGoogle Scholar

  • [71] J. Auerbach, H. Barton, T. Blunt, V. Chaganti, B. Ghai, A. Meng, et al., Using data science as a community advocacy tool to promote equity in urban renewal programs: An analysis of Atlanta’s Anti-Displacement Tax Fund, In: Data Science for Social Good Conference 2017, Sep. 28-29, 2017, Chicago, IL, http://dssg.uchicago.edu/wp-content/uploads/2017/09/auerbach.pdfGoogle Scholar

  • [72] V. Dignum, F. Dignum, Societal challenges need social agents, In: AAAI 2017 Spring Symposium on Artificial Intelligence for the Social Good, https://aaai.org/ocs/index.php/SSS/SSS17/paper/view/15302Google Scholar

  • [73] M. Prasad, Back to the future: A framework for modelling altruistic intelligence explosions, In: AAAI 2017 Spring Symposium on Artificial Intelligence for the Social Good, https://aaai.org/ocs/index.php/SSS/SSS17/paper/view/15326Google Scholar

  • [74] Y.-H. Wang, Y.-Y. Chen, S.-C. Chen, C.-K. Liu, T. C. Hsieh, Data for Social Good: A case study of building an effective public-private partnership on domestic violence prevention, In: Data Science for Social Good Conference 2017, Sep. 28- 29, 2017, Chicago, IL, http://dssg.uchicago.edu/wp-content/uploads/2017/09/hsieh.pdfGoogle Scholar

  • [75] A. Delaunay, J. Guérin, Wandering detection within an embedded system for Alzheimer suffering patients, In: AAAI 2017 Spring Symposium on Artificial Intelligence for the Social Good, https://aaai.org/ocs/index.php/SSS/SSS17/paper/view/15317Google Scholar

  • [76] O. Bendel, LADYBIRD: The animal-friendly robot vacuum cleaner, In: AAAI 2017 Spring Symposium on Artificial Intelligence for the Social Good, https://aaai.org/ocs/index.php/SSS/SSS17/paper/view/15277Google Scholar

  • [77] P. Baumgartner, Challenges in assessing predictive bias, In: Data Science for Social Good Conference 2017, Sep. 28- 29, 2017, Chicago, IL, http://dssg.uchicago.edu/wp-content/uploads/2017/09/baumgartner.pdfGoogle Scholar

  • [78] K. Gummadi, A. Weller, Cross-cultural perceptions of fairness in algorithmic decision making: A case study of criminal risk prediction, Presentation at The AI for Good Global Summit 2018, Geneva, May 15-17, 2018, https://www.itu.int/en/ITUT/AI/2018/Documents/Presentations/Gummadi%20and%20Weller.pdfGoogle Scholar

  • [79] I. Weber, Using hyper-targeted advertising for public health messaging, Presentation at The AI for Good Global Summit 2018, Geneva, May 15-17, 2018, https://www.itu.int/en/ITU-T/AI/2018/Documents/Presentations/Ingmar%20Weber.pdfGoogle Scholar

  • [80] E. Santo, UMO. Smarter Cities. Happier people. AI for real urban sustainability, Presentation at The AI for Good Global Summit 2018, Geneva, May 15-17, 2018, https://www.itu.int/en/ITU-T/AI/2018/Documents/Presentations/Eyal%20Santo.pdfGoogle Scholar

  • [81] M. Chappelka, J. Oh, D. Scott, M. Walker-Holmes, Food for thought: Analyzing public opinion on the supplemental nutrition assistance program, In: Data Science for Social Good Conference 2017, Sep. 28-29, 2017, Chicago, IL, http://dssg.uchicago.edu/wp-content/uploads/2017/09/scott.compressed.pdfGoogle Scholar

  • [82] C.-K. Liu, T. C. Hsieh, Lessons learned from using data science to empower change agents across data silos, In: Data Science for Social Good Conference 2017, Sep. 28-29, 2017, Chicago, IL, https://dssg.uchicago.edu/wp-content/uploads/2017/09/liu.pdfGoogle Scholar

  • [83] E. Nwankwo, Building trust with East African farmers: A poultry app for Good, Presentation at The AI for Good Global Summit 2018, Geneva, May 15-17, 2018, https://www.itu.int/en/ITU-T/AI/2018/Documents/Presentations/Dina%20and%20Ezinne.pdfGoogle Scholar

  • [84] H. Holmestad, Predicting risk of long-term unemployment, In: Data Science for Social Good Conference 2017, Sep. 28-29, 2017, Chicago, IL, https://dssg.uchicago.edu/wp-content/uploads/2017/09/holmestad.pdfGoogle Scholar

  • [85] S. Schiffner, B. Berendt, T. Siil, M. Degeling, R. Riemann, F. Schaub, et al., Towards a roadmap for privacy technologies and the General Data Protection Regulation: A transatlantic initiative, In: Proceedings of the Annual Privacy Forum 2018, Jun. 13- 14, 2018, Barcelona, Springer, Berlin, 2018Google Scholar

  • [86] A. Morton, B. Berendt, S. Gürses, J. Pierson, "Tool Clinics": Embracing multiple perspectives in privacy research and privacysensitive design, Dagstuhl Reports, 2013, 3(7), 96-104Google Scholar

  • [87] A. Genus, Rethinking constructive technology assessment as democratic, reflective, discourse, Technological Forecasting and Social Change, 2006, 73(1), 13-26Google Scholar

  • [88] H. Hanson, Nixon aides suggest colleague was kidding about drug war being designed to target black people, The Huflngton Post, Mar. 25, 2016, http://www.huflngtonpost.com/entry/richard-nixon-drug-warjohn-ehrlichman_us_56f58be6e4b0a3721819ec61Google Scholar

  • [89] Wikipedia contributors, Nuremberg Code, In: Wikipedia, The Free Encyclopedia, 2018, https://en.wikipedia.org/w/index.php?title=Nuremberg_Code&oldid=848155807Google Scholar

  • [90] N. A. Patel, G. D. Elkin, Professionalism and conflicting interests: The American Psychological Association’s involvement in torture, AMA Journal of Ethics, 2015, 17(10), 924-930.Google Scholar

About the article

Received: 2018-01-01

Accepted: 2018-10-27

Published Online: 2019-01-11

Published in Print: 2019-01-01


Citation Information: Paladyn, Journal of Behavioral Robotics, Volume 10, Issue 1, Pages 44–65, ISSN (Online) 2081-4836, DOI: https://doi.org/10.1515/pjbr-2019-0004.

Export Citation

© by Bettina Berendt, published by De Gruyter. This work is licensed under the Creative Commons Attribution 4.0 Public License. BY 4.0

Comments (0)

Please log in or register to comment.
Log in