Democratizing Algorithmic Fairness

Abstract

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as biased. While researchers have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize 'fairness' in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of 'fairness' into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, 'fairness' in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework.

Community-in-the-loop: towards pluralistic value creation in AI, or—why AI needs business ethics

“…Against this background, it seems reasonable to assume that an ethical assessment should take into account the business context of AI systems. In fact, even if an AI system is completely ethically designed on a technical level-if this is possible at all and whatever that may mean with regard to say fairness, privacy or safety in particular [13,44,68]-major ethical questions may arise. Think, for example, of the risks of dual use or the cases in which employees from Google or Microsoft have voiced public protest against the potential use of some of their companies' products for immigration and law enforcement agencies, military purposes or foreign governments [64,69,70].…”

“…In this way, the ambition is to make the use of AI a matter of pluralistic value creation. Thus, acknowledging the political dimension of AI ethics, our approach of deliberative order ethics helps to address the fundamental normative questions raised by the use of AI in society [7][8][9][10][11][12][13].…”

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…Although all researchers of this category state that an ethical framework is needed to use AI in organizational decision making, there is no agreement on the design. Some recommend an implementation of decision rules into AI systems (Webb et al 2019;Wong 2019), while others concentrate on making the machine learn moral guidelines by itself (Bogosian 2017), relating to top-down and bottom-up approaches of AI.…”

“…In summary, articles on ethics are as divided as the topic of AI itself. ''Legal and safety-based frameworks (…) are perhaps best suited to the more narrow AI which is likely to be developed in the near to mid-term'' (Vamplew et al 2018: 31), and they, therefore, seem to be the only frameworks agreed on as a guiding principle (Etzioni and Etzioni 2016;Wong 2019). Researchers thus assume that including ethical guidelines into algorithms is only possible to a limited extent and is always influenced by the people designing them, although several researchers have proposed tools to support this inclusion (Cervantes et al 2016;Etzioni and Etzioni 2016;Giubilini and Savulescu 2018;Vamplew et al 2018).…”

Transparency As Design Publicity: Explaining and Justifying Inscrutable Algorithms

“…Performance metrics provide an important indication of the extent to which every value has been achieved, which is especially important for the overall justification of the system when a value can only be achieved at the expense of another value. For example, fairness can only be pursued at the expense of efficiency; in the case of the recidivism predictions, optimizing for fairness measures leads to a partial failure to release some high-risk detainees, that are mistakenly classified as low-risk, or a partial failure to release low-risk ones (Corbett-Davies et al 2017;Wong 2019). Performance transparency provides an indication of the degree to which both values, of efficiency and (quantified) fairness have been sacrificed.…”