The Ethics of Algorithms: Mapping the Debate
B. Mittelstadt, P. Allo, M. Taddeo, S. Wachter, L. Floridi
More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can have severe consequences affecting individuals as well as groups and whole societies. This paper […]
Accountability for the Use of Algorithms in a Big Data Environment
A. Vedder, L. Naudts
Decision makers, both in the private and public sphere, increasingly rely on algorithms operating on Big Data. As a result, special mechanisms of accountability concerning the making and deployment of algorithms is becoming more urgent. In the upcoming EU General Data Protection Regulation, concepts such as accountability and transparency are guiding principals. Yet, the authors […]
Accountable Algorithms
J. A. Kroll, J. Huey, S. Barocas, E. W. Felten, J. R. Reidenberg, D. G. Robinson, H. Yu
Many important decisions historically made by people are now made by computers. Algorithms can count votes, approve loan and credit card applications, target citizens or neighborhoods for police scrutiny, select taxpayers for an audit, and grant or deny immigration visas. This paper argues that accountability mechanisms and legal standards that govern such decision processes have […]
Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems
A. Datta, S. Sen, Y. Zick
In this paper, the authors have developed a formal foundation to improve the transparency of such decision-making systems. Specifically, they introduce a family of Quantitative Input Influence (QII) measures that attempt to capture the degree of influence of inputs on outputs of systems. These measures can provide a foundation for the design of transparency reports […]
Data-Driven Discrimination at Work
P. T. Kim
A data revolution is transforming the workplace. Employers are increasingly relying on algorithms to decide who gets interviewed, hired, or promoted. Although algorithms can help to avoid biased human decision-making, they also risk introducing new sources of bias. Data mining techniques may cause employment decisions to be based on correlations rather than causal relationships; they […]
Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US
T. Gebru, J. Krause, Y. Wang, Duyun Chen, J. Deng, E. Lieberman Aiden, L. Fei-Fei
As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative to human review. Here, the authors present a method that attempts to determine socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based […]
Tackling the Algorithmic Control Crisis – the Technical, Legal, and Ethical Challenges of Research into Algorithmic Agents
B. Bodo, N. Helberger, K. Irion, F. Zuiderveen Borgesius, Moller, J. Moller, B. van der Velde, N. Bol, B. van Es, C. de Vreese
The objectives of this paper are two-fold. The authors’ first aim is to describe one possible approach to researching the individual and societal effects of algorithmic recommenders, and to share experiences with the readers. The second aim is to contribute to a more fundamental discussion about the ethical and legal issues of “tracking the trackers,” […]
Why a Right to Explanation of Automated Decision Making Does Not Exist in the General Data Protection Regulation
S. Wachter, B. Mittelstadt, L. Floridi
This paper argues that the GDPR lacks precise language as well as explicit and well-defined rights and safeguards against harmful automated decision-making, and therefore runs the risk of being toothless. The authors propose a number of legislative steps that, they argue, would improve the transparency and accountability of automated decision-making when the GDPR comes into […]
Exposure Diversity as a Design Principle for Recommender Systems
N. Helberger, K. Karpinnen & L. D’Acunto
Some argue that algorithmic filtering and adaption of online content to personal preferences and interests is often associated with a decrease in the diversity of information to which users are exposed. Notwithstanding the question of whether these claims are correct, this paper discusses whether and how recommendations can also be designed to stimulate more diverse […]