Information and Communication Technologies (ICT) are continuously shaping the everyday life of billion of people. The impacts on public and private organizations and the products that they offer are already tangible before our eyes, both in terms of benefits and risks. However, while the enthusiasm about the positive effects of developing new technologies has been always at the center of attention, concerns about their impact on humans and society have less been part of the public and scientific debate. Recently, we have seen a change of this trend, thanks also to several initiatives of many institutions (FAT/ML, ACM FAT* among the others), which are encouraging the various communities to discuss AI-related ethical, legal and economic issues.
The aim of this group is to stimulate the debate related to FATE in ICT within the UPF, and in particular the DTIC community. The rich diversity of research fields presents in our department provides an heterogeneous space, and at the same time the FATE framework provides a common ground for debating, from which we imagine that every participant can benefit.
In the first iteration (Spring/Summer 2019), we have invited five speakers who will present how in different fields the use of “intelligent systems” are already impacting our society.
|Automation of personal data and consumer law enforcement using AI||Francesca Lagioia|
|Human behaviour and machine intelligence||Emilia Gomez|
|Big Data, Machine Learning and Justice||Ricardo Baeza-Yates|
|Profiling and automated decision making under the General Data Protection Regulation||Antoni Rubí-Puig|
|Impact of machine intelligence in healthcare (*)||Sergio Sánchez-Martínez|
(*) Note that, in addition, on July 5th BCN MedTech at DTIC-UPF organises with EC's JRC the full-day "Workshop on the impact of artificial intelligence in healthcare" (Details and registration)
Automation of personal data and consumer law enforcement using AI
by Francesca Lagioia, postdoctoral research fellow at Interdepartmental Centre for Research in the History, Philosophy, and Sociology of Law and in Computer Science and Law (CIRSFID) at the University of Bologna, and Research Associate at the European University Institute (EUI)
Despite the European Consumer Law and EU General Data Protection Regulation (GDPR) are in place, and despite enforcers’ competence for abstract control, Terms of Services and Privacy Policies of online services still often fail to comply with regulations. Artificial intelligence and in particular machine learning methods can be used for automating the legal evaluation of both terms of services and privacy policies, in order to empower the civil society representing the interests of consumers.
- Lippi, M., Pałka, P., Contissa, G., Lagioia, F., Micklitz, H. W., Sartor, G., & Torroni, P. (2019). CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service. Artificial Intelligence and Law, 27(2), 117–139. https://doi.org/10.1007/s10506-019-09243-2
Human behaviour and machine intelligence
by Emilia Gomez, lead scientist of the HUMAINT project at the Centre for Advanced Studies, Joint Research Centre, European Commission, and head of the MIR (Music Information Research) lab of the Music Technology Group (MTG) at UPF.
AI systems are already embedded in our daily life, e.g. when finding our way in the city or listening to music. In this lecture we will provide some examples of the impact that AI, in particular data-driven machine learning algorithms and social robots, have in human behavior. We will focus on four main use cases: ML algorithms for decision making, child-robot interaction, AI impact on tasks that we do at work, and music & well being. We will explain the challenges of assessing this impact and making sure those systems are developed “with” and “for” people's welfare
- Gomez Gutierrez, E. et al. (2018). Assessing the impact of machine intelligence on human behaviour: an interdisciplinary endeavour. Joint Research Centre Conference and Workshop Reports. https://arxiv.org/abs/1806.03192
Big Data, Machine Learning and Justice
In this presentation we start with the main challenges of using big data and machine learning, including scalability and fairness. We exemplify these challenges analyzing how machine learning has been applied in justice and how human biases are exposed by models that learn from human data. However, even though these models are not perfect, they are many times better than humans because they are always coherent in their decisions. We will finish with some bad and good practices that should be avoided or enforced, respectively, when training machine learning models.
- Berk at al. Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods & Research 1-42, 2018. https://arxiv.org/abs/1703.09207
- Kleinberg et al. Human Decisions and Machine Predictions, Quarterly Journal of Economics, 237-293, 2018. https://www.nber.org/papers/w23180
Profiling and automated decision making under the General Data Protection Regulation
The session aims at discussing the rules on profiling and automated individual decision-making under the General Data Protection Regulation and at assessing their adequacy from the standpoint of the Fairness, Accountability, Transparency and Ethics (FATE) framework. Our focus will be on profiling, that is, the automated processing of personal data with the goal of evaluating certain personal aspects relating to an individual, such as for instance analyzing or predicting her performance at work, economic situation, health, personal preferences, interests, willingness to pay, reliability, behavior, location or movements.
Progresses in technology and the use of big data analytics, artificial intelligence and machine learning have made it easier to elaborate profiles. Profiling can be in the advantage of individuals and organizations as it may lead to increased efficiencies and cost savings. However, it also involves significant risks for individuals’ rights and freedoms, not only to privacy. Rules in the GDPR aim at finding a balance between those advantages and risks but the final trade-off may not be the most adequate one for the FATE framework.
- Edwards, L. & Veale, M. (2018), Enslaving the Algorithm: From a ‘Right to an Explanation’ to a ‘Right to Better Decisions’?, IEEE Security & Privacy (2018) 16(3), pp. 46-54, DOI: 10.1109/MSP.2018.2701152. Available at SSRN: https://ssrn.com/abstract=3052831 or http://dx.doi.org/10.2139/ssrn.3052831
- Wachter, S. & Mittelstadt, B. (2019), A Right to Reasonable Inferences: Re-Thinking Data Protection Law in the Age of Big Data and AI, Columbia Business Law Review, 2019(1) (forthcoming). Available at SSRN: https://ssrn.com/abstract=3248829 (especially, pp. 4-9 and 120-130).
Impact of machine intelligence in healthcare
by Sergio Sánchez-Martínez, Postdoctoral Research Fellow at Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
This talk aims at advancing the scientific understanding of machine learning (ML) related to healthcare and at studying the impact of ML algorithms on humans, focusing on clinical decision-making. The talk will be articulated around the essential building blocks to achieving the high-level task of clinical decision-making, namely data acquisition, feature extraction, interpretation and decision support. For each of these blocks, the speaker will provide a concise review of state-of-the-art applications, followed by the challenges still to overcome and the potential benefits of their application in clinical practice. At the end, there will be a discussion on the main problems to tackle when creating algorithms to analyze clinical data and also implementation challenges, such as which interaction paradigms we should use, or the competences medical doctors should have.
- Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
- D’hooge, J., & Fraser, A. G. (2018). Learning About Machine Learning to Create a Self-Driving Echocardiographic Laboratory. Circulation, 138(16), 1636–1638. https://doi.org/10.1161/CIRCULATIONAHA.118.037094
- Doshi-Velez, F., & Kim, B. (2017). Towards A Rigorous Science of Interpretable Machine Learning. Retrieved from http://arxiv.org/abs/1702.08608