Skip to content
BY 4.0 license Open Access Published by De Gruyter Open Access January 11, 2019

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

  • Bettina Berendt EMAIL logo

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.

References

[1] L. Pangrazio, Exploring provocation as a research method in the social sciences, International Journal of Social Research Methodology, 2017, 20(2), 22510.1080/13645579.2016.1161346Search in Google Scholar

[2] D. Boyd, K. Crawford, Critical questions for big data, Information, Communication & Society, 2012, 15(5), 662-67910.1080/1369118X.2012.678878Search in Google Scholar

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

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

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

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

[9] K. Popper, The Open Society and Its Enemies , Routledge, UK, 1945Search in Google 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.pdfSearch in Google 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, 407-437Search in Google Scholar

[12] G. Capoccia, Militant democracy: The institutional bases of democratic self-preservation, Annual Review of Law and Social Science, 2013, 9(1), 207-22610.1146/annurev-lawsocsci-102612-134020Search in Google 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), 459-46910.1080/0144929X.2016.1242653Search in Google Scholar

[14] World Economic Forum, Artificial Intelligence for the Common Good, Sustainable, Inclusive and Trustworthy, 2018, https://weforum.ent.box.com/v/AI4GoodSearch in Google 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-goodSearch in Google Scholar

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

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

[19] L. Cao, Data Science: A comprehensive overview, ACM Computing Surveys, 2017, 50(3), Article No. 4310.1145/3076253Search in Google Scholar

[20] D. Conway, The Data Science Venn Diagram (n.d.), http://drewconway.com/zia/2013/3/26/the-data-sciencevenn- diagramSearch in Google 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=3277633Search in Google Scholar

[22] S. J. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, Englewood Cliffs, NJ, 1995Search in Google 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, 82-88Search in Google Scholar

[24] G. Veruggio, Roboethics [TC Spotlight], IEEE Robotics&Automation Magazine, 2010, 17(2), 105-10910.1109/MRA.2010.936959Search in Google Scholar

[25] P. M. Asaro, What should we want from a robot ethic? International Review of Information Ethics, 2006, 6, 9-1610.29173/irie134Search in Google 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), 243-25610.1007/s10676-015-9367-8Search in Google Scholar

[27] W. Wallach, C. Allen, Moral machines: Teaching robots right from wrong, Oxford University Press, New York, NY, 2008Search in Google 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, 25-4610.1007/978-3-319-09798-5_2Search in Google 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.pdfSearch in Google Scholar

[30] J. Goguen, Requirements engineering as the reconciliation of technical and social issues, In: Requirements Engineering: Social and Technical Issues, Academic Press, 1994, 165-199Search in Google 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), 883-89110.1111/add.13265Search in Google Scholar PubMed PubMed Central

[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-violenceSearch in Google Scholar

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

[34] P. Naur, Formalization in program development, BIT Numerical Mathematics, 1982, 22(4), 437-45310.1007/BF01934408Search in Google 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=3Search in Google 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.pdfSearch in Google 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/Search in Google Scholar

[38] J. Mechanic, When a drug epidemic hit white America, addiction became a disease, Huffington Post, Jul. 10, 2017, https://www.huffingtonpost.com/entry/when-a-drug-epidemic-hit-whiteamerica-addiction-became_us_5963a588e4b08f5c97d06b9aSearch in Google Scholar

[39] K. Lum, W. Isaac, To predict and serve?, Significance, 2016, 13(5), 14-1910.1111/j.1740-9713.2016.00960.xSearch in Google Scholar

[40] A. Chouldechova, Fair prediction with disparate impact: A study of bias in recidivism prediction instruments, Big Data, 2017, 5(2), 153-16310.1089/big.2016.0047Search in Google Scholar PubMed

[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), 201710.1145/3219617.3219634Search in Google 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, 17-33Search in Google 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/Search in Google Scholar

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

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

[46] E. Bakshy, S. Messing, L. A. Adamic, Exposure to ideologically diverse news and opinion on Facebook, Science, 2015, 348(6239), 1130-113210.1126/science.aaa1160Search in Google Scholar PubMed

[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 3782510.1038/srep37825Search in Google Scholar PubMed PubMed Central

[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, 126-13810.1016/j.gloenvcha.2015.03.006Search in Google 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, 201410.1145/2566486.2568012Search in Google 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), 2016012610.1098/rsta.2016.0126Search in Google Scholar PubMed PubMed Central

[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, 2017Search in Google 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.pdfSearch in Google Scholar

[53] M. Kunaver, T. Požrl, Diversity in recommender systems - a survey, Knowledge-Based Systems, 2017, 123, 154-16210.1016/j.knosys.2017.02.009Search in Google 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.pdfSearch in Google 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), 1-23510.1561/1100000028Search in Google Scholar

[56] R. Shamir, The age of responsibilization: on market-embedded morality, Economy and Society, 2008, 37(1), 1-1910.1080/03085140701760833Search in Google 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/datafictionSearch in Google Scholar

[58] M. Gross, L. McGoey (Eds.), Routledge International Handbook of Ignorance Studies, Routledge, London / New York, 201510.4324/9781315867762Search in Google 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, 2462-247110.1016/j.procs.2014.05.230Search in Google Scholar

[60] M. Kosinski, D. Stillwell, T. Graepel, Private traits and attributes are predictable from digital records of human behavior, PNAS, 2013, 110(15), 5802-580510.1073/pnas.1218772110Search in Google Scholar PubMed PubMed Central

[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-farSearch in Google 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.247789910.2139/ssrn.2477899Search in Google Scholar

[63] C. O’Neil, Weapons ofMath Destruction, Crown Publishers, New York, 2016Search in Google 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), 135-15210.1089/big.2016.0055Search in Google Scholar PubMed

[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.pdfSearch in Google Scholar

[66] E. Morozov, To Save Everything, Click Here: The Folly of Technological Solutionism, Public Affairs, New York, 2013Search in Google 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/Search in 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.phpSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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/15302Search in Google 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/15326Search in Google 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.pdfSearch in Google 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/15317Search in Google 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/15277Search in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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.pdfSearch in Google 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, 201810.1007/978-3-030-02547-2_2Search in Google 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), 96-104Search in Google Scholar

[87] A. Genus, Rethinking constructive technology assessment as democratic, reflective, discourse, Technological Forecasting and Social Change, 2006, 73(1), 13-2610.1016/j.techfore.2005.06.009Search in Google Scholar

[88] H. Hanson, Nixon aides suggest colleague was kidding about drug war being designed to target black people, The Huffington Post, Mar. 25, 2016, http://www.huffingtonpost.com/entry/richard-nixon-drug-warjohn-ehrlichman_us_56f58be6e4b0a3721819ec61Search in Google Scholar

[89] Wikipedia contributors, Nuremberg Code, In: Wikipedia, The Free Encyclopedia, 2018, https://en.wikipedia.org/w/index.php?title=Nuremberg_Code&oldid=848155807Search in Google 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), 924-930.10.1001/journalofethics.2015.17.10.nlit1-1510Search in Google Scholar PubMed

Received: 2018-01-01
Accepted: 2018-10-27
Published Online: 2019-01-11

© 2019 Bettina Berendt, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Downloaded on 28.11.2023 from https://www.degruyter.com/document/doi/10.1515/pjbr-2019-0004/html
Scroll to top button