Artificial Intelligence and Big Data Analytics Management, Governance, and Compliance

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https://hdl.handle.net/10125/63704

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    Preventing Algorithmic Bias in the Development of Algorithmic Decision-Making Systems: A Delphi Study
    ( 2020-01-07) Aysolmaz, Banu ; Iren, Deniz ; Dau, Nancy
    In this digital era, we encounter automated decisions made about or on behalf of us by the so called Algorithmic Decision-Making (ADM) systems. While ADM systems can provide promising business opportunities, their implementation poses numerous challenges. Algorithmic bias that can enter these systems may result in systematical discrimination and unfair decisions by favoring certain individuals over others. Several approaches have been proposed to correct erroneous decision-making in the form of algorithmic bias. However, proposed remedies have mostly dealt with identifying algorithmic bias after the unfair decision has been made rather than preventing it. In this study, we use Delphi method to propose an ADM systems development process and identify sources of algorithmic bias at each step of this process together with remedies. Our outputs can pave the way to achieve ethics-by-design for fair and trustworthy ADM systems.
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    Artificial Intelligence Regulation: A Meta-Framework for Formulation and Governance
    ( 2020-01-07) Almeida, Patricia ; Santos, Carlos ; Farias, Josivania Silva
    This article presents a meta-framework for Artificial Intelligence (AI) regulation that encompasses all stages of international public policy-making, from formulation to sustainable governance. Based on a vast systematic review of the literature on Artificial Intelligence Regulation (AIR) published between 2009 and 2019, a dispersed body of knowledge organized under the label “framework” was identified, containing 15 unique frameworks and several different theories that created a complex scientific scenario for research and practice. Theories and principles as diverse as Agile and Ethics were found. Thus, a structured analytical method was followed to integrate this bulk of knowledge into a cohesive, synthetic, and generic theoretical tool. The resulting “AIR framework” provides a trustworthy lens for societies to think collectively and make informed policy decisions related to what, when, and how the uses and applications of AI should be regulated. Moreover, the novel framework organizes the latest developments in the area in a format that allows future research to be framed in and added to the published literature. The (potential) impacts of AI on society are immense, and therefore the discourses, social negotiations, and applications of this technology should be guided by common grounds in terms of terminology, governance, and social values.
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