Ethical Implications and Accountability of Algorithms

By Kirsten E. Martin. Journal of Business Ethics, 2018

530 citations recorded by the source corpus.

Algorithms silently structure our lives. Algorithms can determine whether someone is hired, promoted, offered a loan, or provided housing as well as determine which political ads and news articles consumers see. Yet, the responsibility for algorithms in these important decisions is not clear. This article identifies whether developers have a responsibility for their algorithms later in use, what those firms are responsible for, and the normative grounding for that responsibility. I conceptualize algorithms as value-laden, rather than neutral, in that algorithms create moral consequences, reinforce or undercut ethical principles, and enable or diminish stakeholder rights and dignity. In addition, algorithms are an important actor in ethical decisions and influence the delegation of roles and responsibilities within these decisions. As such, firms should be responsible not only for the value-laden-ness of an algorithm but also for designing who-does-what within the algorithmic decision. As such, firms developing algorithms are accountable for designing how large a role individual will be permitted to take in the subsequent algorithmic decision. Counter to current arguments, I find that if an algorithm is designed to preclude individuals from taking responsibility within a decision, then the designer of the algorithm should be held accountable for the ethical implications of the algorithm in use.

Subfields: Cognitive Neuroscience, Information Systems, Safety Research

View Paper: https://www.semanticscholar.org/paper/81528438e75fe9ff086113651bd108a33baf9ed9

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Citations in the corpus

The ethics of algorithms: key problems and solutions (2020, background): “In this respect, Martin (2019) stresses that, while the difficulty of explaining ML algorithms’ outputs is certainly real, it is important not to let this difficulty incentivise organisations to develop complex systems to shirk responsibility.”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “1 3 code of an algorithm itself or in the data with which the algorithm was trained (Martin 2018).”
Trustworthy Artificial Intelligence: A Review (2022, background): “The first step is to ensure that the data used for training is diverse and does not have any bias [55, 132, 188].”
Algorithms, Platforms, and Ethnic Bias: An Integrative Essay Forthcoming in Phylon: The Clark Atlanta University Review of Race and Culture (2018, unknown): “More recently, not only the popular press but also the social sciences are considering the implications of digital bias (Martin, 2018).”
What to account for when accounting for algorithms: a systematic literature review on algorithmic accountability (2020, background): “Developers are often seen as the responsible party for such questions as they are ‘knowledgeable as to the design decisions and [are] in a unique position to inscribe the algorithm with the value-laden biases as well as roles and responsibilities of the algorithmic decision’ [101].”
Accountability in artificial intelligence: what it is and how it works (2023, unknown): “This is also the case in the HLEG report on Ethics guidelines for trustworthy AI (2019), which informed the AIA.”