AI is becoming a sophisticated tool in the hands of a variety of stakeholders, including political leaders. Some AI applications may raise new ethical and legal questions, and in general have a significant impact on society (for the good or for the bad or for both).
People’s motivation plays a key role here. With AI the important question is how to avoid that it goes out of control, and how to understand how decisions are made and what are the consequences for society at large.
Students will learn the ethical implications of the use of Artificial Intelligence (AI).
What are the consequences for society? For human beings / individuals? Does AI serve human kind?
Discussion and debate of ethical issues is an essential part of professional development—both within and between disciplines—as it can establish a mature community of responsible practitioners.
Through ethical reflection students can gain orientation / competencies that will help them in their ethical decision making.
Students will work in small groups and learn how to apply an AI Ethical Inspection Process, called Z-inspection, to real AI use cases.
The course will provide an ethical framework (called Z-inspection), domain-specific resources, metrics, processes, tools and case studies, to guide teams of students in their efforts to assess ethical issues in AI, such as for example:
- Transparencies / Explainability/ intelligibility/interpretability;
- Privacy/ Responsibility/Accountability;
- Human-in the loop.
Z-inspection is being currently developed by the team of Prof. Zicari at the Frankfurt Big Data Lab and it could be part of an Ethics by Design process, or if the AI has al- ready been designed, it can be used to do an ethical sanity check, so that a certain AI Ethical standard of care is achieved. It can be used by a variety of AI stakeholders
The overall goal of the course is to contribute to closing the gap between “principles” (the “what” of AI ethics) and “practices” (the ”how”).
Students should have an interest in reflecting on what is right or wrong, and it is assumed that they are capable of discussing a scenario and taking a view on whether an action is ethical.
We encourage students with different backgrounds, knowledge, and geographies to enroll in this course. The topic is highly interdisciplinary and therefore requires different points of views, expertise, and attitudes.
Course Registration and Schedule webpage!
Communication via Email: EthicalAISS2020@gmail.com
Ethical Implications of AI: How to get credit points
Assignments – Each student, individually, will read 1 selected report/paper every week. For each paper you need to answer two questions in written form. For each question satisfactorily answered you get 1 Point.
In order to get the final credit points you need to have at least a total of 7 Points.
Reports/Papers classified by topics
The Ethics of Artificial Intelligence (AI), AI and Trust.
 Ethical and societal implications of algorithms, data, and artificial intelligence: a roadmap for research. Whittlestone, J. Nyrup, R. Alexandrova, A. Dihal, K. Cave, S. (2019), London. Nuffield Foundation. Link to .PDF
 Ethics Guidelines for Trustworthy AI. Independent High-Level Expert Group on Artificial Intelligence. European commission, 8 April, 2019. Link to .PDF
 WHITE PAPER. On Artificial Intelligence – A European approach to excellence and trust. European Commission, Brussels, 19.2.2020 COM(2020) 65 final. Link to .PDF
Ethics, Moral Values, Humankind, Technology, AI Examples.
 Perspectives on Issues in AI Governance, Lynette Webb, Charina Chou, Google White Paper, 2019. Link to .PDF
 AI on the Case: Legal and Ethical Issues. Richard Austin, Deeth Williams Wall LLP , May 17, 2019. Link to .PDF
 Report on Algorithmic Risk Assessment Tools in the U.S. Criminal Justice System, Partnership on AI, 2019. Link
Dirty Data, Bad Predictions: How Civil Rights Violations Impact Police Data, Predictive Policing Systems, and Justice (February 13, 2019). Richardson, Rashida and Schultz, Jason and Crawford, Kate, 94 N.Y.U. L. REV. ONLINE 192 (2019). Available at SSRN
Fairness, Bias and Discrimination in AI. From Philosophy to Machine Learning.
 Improving Fairness in Machine Learning Systems: What Practitioners Need? K. Holstein et al. CHI 2019; May 4-0, 2019. Link to .PDF
 Ensuring, Fairness in Machine Learning to Advance Health, Alvin Rajkomar et al. Equity, Annals of Internal Medicine (2018). DOI: 10.7326/M181990. Link to .PDF
 Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements. Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Allison Woodruff, Christine Luu, Pierre Kreitmann, Jonathan Bischof, Ed H. Chi (Submitted on 14 Jan 2019). Link to .PDF
 Dissecting racial bias in an algorithm used to manage the health of populations. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Science. 2019 Oct 25;366(6464):447-453. doi: 10.1126/science.aax2342. Link to .PDF
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias, Rachel K. E. Bellamy, Kuntal Dey, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Kalapriya Kannan, Pranay Lohia, Jacquelyn Martino, Sameep Mehta, Aleksandra Mojsilovic, Seema Nagar, Karthikeyan Natesan Ramamurthy, John Richards, Diptikalyan Saha, Prasanna Sattigeri, Moninder Singh, Kush R. Varshney, Yunfeng Zhang, 2018, Paper link, Open Source project link, Published as IBM Journal of Research and Development 63(4/5), 2019
AI: Explainability, Transparency.
 Experiences with Improving the Transparency of AI Models and Services. Michael Hind, Stephanie Houde, Jacquelyn Martino, Aleksandra Mojsilovic, David Piorkowski, John Richards, Kush R. Varshney (Submitted on 11 Nov 2019), arXiv:1911.08293v1. Link to .PDF
Petkovic D, Kobzik L, Re C. “Machine learning and deep analytics for biocomputing: call for better explainability”. Pacific Symposium on Biocomputing Hawaii, January 2018;23:623-7, Link to .PDF
Petkovic D, Kobzik L, Ganaghan R,“AI Ethics and Values in Biomedicine – Technical Challenges and Solutions”, Pacific Symposium on Biocomputing, Hawaii January 3-7, 2020, Link to .PDF
Gunning D, Aha D.:”DARPA’s Exianable Artificial Intelligence Program”, AI magazine, Association for the Advancement of Artificial Intelligence, Summer 2019, slides
Ribeiro M, Singh S, Guestrin C. „Why Should I Trust You? Explaining the Predictions of Any Classifier”, KDD ’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningAugust, 2016, Link to .PDF or ACM PDF
Ribeiro M, Singh S, Guestrin C.: ”Nothing Else Matters: Model-Agnostic Explanations by Identifying Prediction Invariance”, 30th Conf. of Neural Information Processing Systems (NIPS 2016), Barcelona, Spain 2016, Link to .PDF
Petkovic D, Altman R, Wong M, Vigil A.: “Improving the explainability of Random Forest classifier – user centered approach”. Pacific Symposium on Biocomputing. 2018;23:204-15, Link to .PDF
D. Petkovic, A. Alavi, D. Cai, J. Yang, S. Barlaskar: “RFEX – Simple random Forest Model and Sample Explainer for non-ML experts”, Link to .PDF
Barlaskar S, Petkovic D: “Applying Improved Random Forest Explainability (RFEX 2.0) on synthetic data”, SFSU TR 18.01, 11/27/20181; with related toolkit at https://www.youtube.com/watch?v=neSVxbxxiCE
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques, Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang, 2019, Link to .PDF
Explaining explainable AI, Michael Hind, XRDS: Crossroads, The ACM Magazine for Students 25(3), ACM, 2019, Link to .PDF
Explaining decisions made with AI. – ICO and The Alan Turing Institute, May 2020, Link
Human in the loop, Security, and Accountability.
Calvo, R.A., Peters, D. & Cave, S. Advancing impact assessment for intelligent systems. Nat Mach Intell, Vol 2, 89-91 (2020). Link to. PDF
Concrete Problems in AI Safety, Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané. Link to PDF
Anomalous Instance Detection in Deep Learning: A Survey – Saikiran Bulusu, Bhavya Kailkhura, Bo Li, Pramod K. Varshney, Dawn Song. Link to PDF
Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims – Miles Brundage et al. Link to PDF
Introduction to Z-inspection. A framework to assess Ethical AI
“Z-inspection: Towards a process to assess Ethical AI” – Roberto V. Zicari – With contributions from: Irmhild van Halem, Matthew Eric Bassett, Karsten Tolle, Timo Eichhorn, Todor Ivanov, Jesmin Jahan Tithi. CSGI(Cognitive Systems Group) Talk, Oct.31, 2019, Youtube video, Link to .PDF
Assessing AI use cases. Socio-Technical Scenarios.
 Ethical Framework for Designing Autonomous Intelligent Systems. J Leikas et al. J. of Open Innovation, 2019, 5, 1. Link
Assessing AI use cases. Ethical tensions, Trade offs.
 Algorithmic Impact Assessment: A Practical Framework for Public Agency Accountability, Reisman D., Schultz J, Crawford K, Whittake M, AI Now, April 2018. Link to .PDF
 FactSheets: Increasing trust in AI services through suppliers declarations of conformity. Arnold, M.; Bellamy, R. K. E.; Hind, M.; Houde, S.; Mehta, S.; Mojsilovic ́, A.; Nair, R.; Natesan- Ramamurthy, K.; Olteanu, A.; Piorkowski, D.; Reimer, D.; Richards, J.; Tsay, J.; and Varshney, K. R. 2019. IBM Journal of Research & Development 63(4/5). Link to .PDF
 Datasheets for datasets. Gebru, T.; Morgenstern, J.; Vecchione, B.; Vaughan, J. W.; Wallach, H.; Daume ́, III, H.; and Craw- ford, K. 2018.. In Proceedings of the Fairness, Accountability, and Transparency in Machine Learning Workshop. Link to .PDF
 IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems, Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, First Edition. Pp. 211 – 281. Link to .PDF
COVID-19 Rapid Evidence Review: Exit through the App Store?, Nuffield Foundation. Link
AI Ethics in Healthcare
 Stealth research: Lack of peer‐reviewed evidence from healthcare unicorns, Ioana A. Cristea Eli M. Cahan John P. A. Ioannidis, European Journal of Clinical Investigation, 28 January 2019. Link
 Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare, J Med Ethics 2019;0:1–7. doi:10.1136/medethics-2019-105586. Link to .PDF
 Cardisio: https://cardis.io
 Schonberg D. Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications. International Journal of Law and Information Technology, 2019, Link
1) OECD, ‘Recommendation of the Council on Artificial Intelligence’ (22 May 2019), Link
2) G20, ‘G20 Ministerial Statement on Trade and Digital Economy’ (9 June 2019), Link to .PDF
3) Ethics Guidelines for Trustworthy AI. Independent High-Level Expert Group on Artificial Intelligence. European commission, 8 April, 2019, Link to .PDF
4) F Möslein, ‘Robots in the boardroom: artificial intelligence and corporate law’ in W Barfield and U Pagallo (eds), Research Handbook on the Law of Artificial Intelligence (Edward Elgar Publishing 2018), Link to .PDF
5) F Möslein, ‘Regulating Robotic Conduct: On ESMA’s New Guidelines and Beyond’ in N Aggarwal and others (eds), Autonomous Systems and the Law (Beck, Nomos 2019) 45
6) F Möslein ‘Leitlinien für den Einsatz künstlicher Intelligenz’ in D Linardatos (ed), Rechtshandbuch Robo-Advice (Beck, Vahlen 2020) 58
1) Ethical Business Regulation:Understanding the Evidence, Christopher Hodges, Professor of Justice Systems, and Fellow of Wolfson College, University of Oxford February 2016. Link .PDF
2) Ethical Theories, By Larry Chonko, Ph.D. The University of Texas at Arlington, Slides, Notes to slides
German Data Ethics Commision
- Opinion of the Data Ethics Commission (PDF, 2,673 KB, file is barrier-free)
- Opinion of the Data Ethics Commission – Executive Summary (PDF, 777 KB, file is barrier-free)
Roberto V. Zicari, Founder Frankfurt Big Data Lab. Course coordinator
Roberto V. Zicari is professor of Database and Information Systems (DBIS) at the Goethe University Frankfurt, Germany. He is an internationally recognized expert in the field of Databases and Big Data. His interests also expand to Ethics and AI, Innovation and Entrepreneurship. He is the founder of the Frankfurt Big Data Lab at the Goethe University Frankfurt, and the editor of the ODBMS.org web portal and of the ODBMS Industry Watch Blog. He was for the past five years a visiting professor with the Center for Entrepreneurship and Technology within the Department of Industrial Engineering and Operations Research at UC Berkeley (USA).
Prof. Dr. Gemma Roig, Group Leader, Computational Vision & Artificial Intelligence, Goethe University Frankfurt
I am currently a professor at the Computer Science Department in Goethe University Frankfurt. I am also a research affiliate at MIT. Before I was ass. prof. at Singapore University of Technology and Design. Previously, I was a postdoc fellow at MIT in the Center for Brains Minds and Machines with Prof. Tomaso Poggio. I was also affiliated at the Laboratory for Computational and Statistical Learning. I pursued my doctoral degree in Computer Vision at ETH Zurich. My research focuses on understanding the underlying computational principles of visual intelligence of humans and artificial systems, with the aim of developing a general artificial intelligence framework. Such general artificial intelligence system, is fundamental to design machine models that mimic or surpass human performance in specific domains, and that can automatically learn new tasks.
|Dr. Emmanuel R. Goffi, Director, Observatoire éthique & intelligence artificielle | Observatory on Ethics & Artificial Intelligence at the Institut Sapiens, in Paris
Emmanuel R. Goffi is an expert in ethics of artificial intelligence. He was the Director of the Creéia – Centre de recherche et expertise en éthique et intelligence artificielle and a Professor of ethics with the ILERI – Institut libre d’étude des relations internationales. He holds a PhD in Political Science from Science Po-CERI. Emmanuel is a research fellow with the Centre for Defence and Security Studies at the University of Manitoba (UofM), in Winnipeg, and a research member with the Centre FrancoPaix at the Université du Québec à Montréal. He is also a member of the Mines Action Canada Board.
|Dr. Thomas Grote, Ethics and Philosophy Lab, Cluster of Excellence: „Machine Learning: New Perspectives for Science“, University of Tübingen, Tübingen 72076, Germany
Dr. Thomas Grote is a postdoctoral researcher at the Ethics and Philosophy Lab (EPL) of the Cluster of Excellence: Machine Learning: New Perspectives for Science at the University of Tübingen. His research focuses on issues related to machine learning at the intersection of epistemology and ethics.
|Prof. Dr. Florian Möslein, Professor of Law at the Philipps-University Marburg, Director of the Institute of the Law and Regulation of Digitalisation (IRDi, www.irdi.institute)
Florian Möslein is Director of the Institute for Law and Regulation of Digitalisation (www.irdi.institute) and Professor of Law at the Philipps-University Marburg, where he teaches Contract Law, Company Law and Capital Markets Law. He previously held academic positions at the Universities of Bremen, St. Gallen, and Berlin, and visiting fellowships in Italy (Florence, European University Institute), the US (Stanford and Berkeley), Australia (University of Sydney), Spain (CEU San Pablo, Madrid) and Denmark (Aarhus).
Having graduated from the Faculty of Law in Munich, he also holds academic degrees from the University of Paris-Assas (licence en droit) and London (LL.M. in International Business Law). Florian Möslein published three monographs and over 80 articles and book contributions, and has edited seven books.
His current research focus is on regulatory theory, corporate sustainability and the legal challenges of the digital age.
|Prof. Dragutin Petkovic, Professor, Associate Chair, Undergraduate Advisor, IEEE LIFE Fellow, Director, Computing for Life Sciences (CCLS), Coordinator for Graduate Certificates in AI Ethics and SW Engineering
Prof. D. Petkovic obtained his Ph.D. at UC Irvine, in the area of biomedical image processing. He spent over 15 years at IBM Almaden Research Center as a scientist and in various management roles. His contributions ranged from use of computer vision for inspection, to multimedia and content management systems. He is the founder of IBM’s well-known QBIC (query by image content) project, which significantly influenced the content-based retrieval field. Dr. Petkovic received numerous IBM awards for his work and became an IEEE Fellow in 1998 and IEEE LIFE Fellow in 2018 for leadership in content-based retrieval area. Dr. Petkovic also had various technical management roles in Silicon Valley startups. In 2003 Dr. Petkovic joined CS Department as a Chair and also founded SFSU Center for Computing for Life Sciences in 2005. Currently, Dr. Petkovic is the Associate Chair of the SFSU Department of Computer Science and Director of the Center for Computing for Life Sciences. He led the establishment of SFSU Graduate Certificate in AI Ethics, jointly with SFSU Schools of Business and Philosophy. Research and teaching interests of Prof. Petkovic include Machine Learning with emphasis on Explainability and Ethics, teaching methods for Global SW Engineering and engineering teamwork, and the design and development of easy to use systems.
|Dr. Christopher Burr is a philosopher of cognitive science and artificial intelligence. He is a Senior Research Associate at the Alan Turing Institute and a Research Associate at the Digital Ethics Lab, University of Oxford.
His current research explores philosophical and ethical issues related to data-driven technologies and human-computer interaction, including the opportunities and risks that such technologies have for mental health and well-being. A primary goal of this research is to develop robust and pragmatic guidance to support the governance, responsible innovation, and sustainable use of data-driven technology within a digital society. To support this goal, he has worked with a number of public sector bodies and organisations, including NHSx; the UK Government’s Department for Health and Social Care; Department for Digital, Culture, Media and Sport; Centre for Data Ethics and Innovation; and the Ministry of Justice. He has held previous posts at the University of Bristol, where he explored the ethical and epistemological impact of big data and artificial intelligence as a postdoctoral researcher and also completed his PhD in 2017. Research Interests: Philosophy of Cognitive Science and Artificial Intelligence, Digital Ethics, Bioethics, Decision Theory, Public Policy, and Human-Computer Interaction.
|DSc. Magnus Westerlund, Principal Lecturer, Head of Master Degree Programme in Big Data Analytics
Arcada University of Applied Sciences, Helsinki, Finland
Magnus Westerlund (DSc) is the programme director of the master degree programme in big data analytics and deputy head of business and analytics department at Arcada University of Applied Sciences in Helsinki, Finland. He has a background from the private sector in telecom and information management and earned his doctoral degree in information systems at Åbo Akademi University, Finland. Magnus has research publications in the fields of analytics, IT-security, cyber regulation, and distributed ledger technology. His current research topics are found in the decentralized platform area of distributed applications, and the application of intelligent and secure autonomous systems. His long-term aim is to help define what we mean by autonomous systems that are trustworthy, accountable, and that can learn from interaction.
|Prof. Rafael A. Calvo, Chair in Engineering Design, Faculty of Engineering, Dyson School of Design Engineering, Imperial College London
Rafael A. Calvo, PhD (2000) is Professor at Imperial College London focusing on the design of systems that support wellbeing in areas of mental health, medicine and education, and the ethical challenges raised by new technologies. In 2015 Calvo was appointed a Future Fellow of the Australian Research Council to study the design of wellbeing-supportive technology.
|Dr. Estella Hebert, Goethe University Frankfurt
Estella Hebert is a postdoctoral researcher and lecturer in the department of education at the Goethe University Frankfurt focusing her research on questions of digitalisation within educational contexts. She finished her PhD on the relationship of identity, agency and personal data in 2019. Coming from a media pedagogical and educational philosophical perspective her interests are based in post-digital perspectives on the social, ethical and cultural transformations caused by digitality, questions of datafication and media critical perspectives.
|Romeo Kienzler, IBM Center for Open Source Data and AI Technologies, San Francisco, CA, USA
Romeo Kienzler is Chief Data Scientist at the IBM Center for Open Source Data and AI Technologies (CODAIT) in San Fransisco. He holds an M. Sc. (ETH) in Computer Science with specialisation in Information Systems, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology Zurich. He works as Associate Professor for Artificial Intelligence at the Swiss University of Applied Sciences Berne and Adjunct Professor for Information Security at the Swiss University of Applied Sciences Northwestern Switzerland (FHNW). His current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, and the Apache Spark stack. Recently he joined the Linux Foundation AI as lead for the Trusted AI technical workgroup with focus on Deep Learning Adversarial Robustness, Fairness and Explainability. He also contributes to various open source projects. He regularly speaks at international conferences including significant publications in the area of data mining, machine learning and Blockchain technologies. Romeo is lead instructor of the Advance Data Science specialisation on Coursera with courses on Scalable Data Science, Advanced Machine Learning, Signal Processing and Applied AI with DeepLearning.
|Carl Mörch, Postdoctoral Fellow, Algora Lab – MILA, OBVIA
Carl is currently a postdoctoral fellow at the Université de Montréal and Mila. He has been awarded a Postdoctoral Fellowship by the International Observatory on the Societal Impacts of Artificial Intelligence and Digital Technologies (OBVIA). He is also a lecturer and adjunct professor at UQÀM (Montréal, Canada). His research is oriented towards the creation of AI Ethics Tools. His objective is to contribute to the concrete application of high-level ethical principles by developing lists of standards in high-risk areas (Health, Finance). In general, he is interested in the responsible development of technologies in society, health care and psychology. He co-created canadaprotocol.com, an open access tool for AI developers working in Mental Health. He is also working on the ethical evaluation of free mobile applications and on the concept of moral competence in AI. Finally, he is leading “Reach Me” an m-health project to improve pregnant women’s access to prenatal services, using text messaging. He holds a M.Psy. (ICP, France), and a Ph.D. in Psychology (UQÀM, Canada).
|Dr. Michael Hind, Distinguished Research Staff Member, IBM Research AI Department, IBM Thomas J Watson Research Center
Dr. Hind has authored over 50 publications, served on over 50 program committees, and given several keynotes and invited talks at top universities, conferences, and government settings. Michael has led dozens of researchers to successfully transfer technology to various parts of IBM and helped launch several successful open source projects, such as AI Fairness 360 and AI Explainability 360. His 2000 paper on Adaptive Optimization was recognized as the OOPSLA’00 Most Influential Paper and his work on Jikes RVM was recognized with the SIGPLAN Software Award in 2012. Michael is an ACM Distinguished Scientist, and a member of IBM’s Academy of Technology.
|Prof. Christiane Wendehorst, Professor of Civil Law at the University of Vienna
Christiane Wendehorst has been Professor of Civil Law at the University of Vienna since 2008. Amongst other functions, she is founding member and President of the European Law Institute (ELI), chair of the Academy Council of the Austrian Academy of Sciences (ÖAW), Co-Head of the Department of Innovation and Digitalisation in Law, and member of the Managing Board of the Austrian Jurists‘ Association (ÖJT), the Academia Europea (AE), the International Academy for Comparative Law (IACL), the American Law Institute (ALI) and the Bioethics Committee at the Austrian Federal Chancellery. She has been Co-chair of the German Data Ethics Committee from 2018-2019. Currently, her work is focussed on legal challenges arising from digitalization, and she has worked as an expert on subjects such as digital content, Internet of Things, artificial intelligence and data economy for, inter alia, the European Commission, the European Parliament, the German Federal Government, the ELI and the ALI. Prior to moving to Vienna, she held chairs in in Göttingen (1999-2008) and Greifswald (1998-99) and was Managing Director of the Sino-German Institute of Legal Studies (2000-2008).