16 research outputs found
Trusting the Moral Judgments of a Robot: Perceived Moral Competence and Humanlikeness of a GPT-3 Enabled AI
Advancements in computing power and foundational modeling have enabled artificial intelligence (AI) to respond to moral queries with surprising accuracy. This raises the question of whether we trust AI to influence human moral decision-making, so far, a uniquely human activity. We explored how a machine agent trained to respond to moral queries (Delphi, Jiang et al., 2021) is perceived by human questioners. Participants were tasked with querying the agent with the goal of figuring out whether the agent, presented as a humanlike robot or a web client, was morally competent and could be trusted. Participants rated the moral competence and perceived morality of both agents as high yet found it lacking because it could not provide justifications for its moral judgments. While both agents were also rated highly on trustworthiness, participants had little intention to rely on such an agent in the future. This work presents an important first evaluation of a morally competent algorithm integrated with a human-like platform that could advance the development of moral robot advisors
Fairness: from the ethical principle to the practice of Machine Learning development as an ongoing agreement with stakeholders
This paper clarifies why bias cannot be completely mitigated in Machine
Learning (ML) and proposes an end-to-end methodology to translate the ethical
principle of justice and fairness into the practice of ML development as an
ongoing agreement with stakeholders. The pro-ethical iterative process
presented in the paper aims to challenge asymmetric power dynamics in the
fairness decision making within ML design and support ML development teams to
identify, mitigate and monitor bias at each step of ML systems development. The
process also provides guidance on how to explain the always imperfect
trade-offs in terms of bias to users
Does the End Justify the Means?:On the Moral Justification of Fairness-Aware Machine Learning
Despite an abundance of fairness-aware machine learning (fair-ml) algorithms,
the moral justification of how these algorithms enforce fairness metrics is
largely unexplored. The goal of this paper is to elicit the moral implications
of a fair-ml algorithm. To this end, we first consider the moral justification
of the fairness metrics for which the algorithm optimizes. We present an
extension of previous work to arrive at three propositions that can justify the
fairness metrics. Different from previous work, our extension highlights that
the consequences of predicted outcomes are important for judging fairness. We
draw from the extended framework and empirical ethics to identify moral
implications of the fair-ml algorithm. We focus on the two optimization
strategies inherent to the algorithm: group-specific decision thresholds and
randomized decision thresholds. We argue that the justification of the
algorithm can differ depending on one's assumptions about the (social) context
in which the algorithm is applied - even if the associated fairness metric is
the same. Finally, we sketch paths for future work towards a more complete
evaluation of fair-ml algorithms, beyond their direct optimization objectives
Five sources of bias in natural language processing
Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures
An ethical matrix for the reintroduction of trafficked primates:a platyrrhine case study
The illegal primate trade is one of the major drivers of the decline of nonhuman primate populations and a threat to their wellbeing. Thousands of trafficked primates enter rescue centers every year, and their destiny (release back into the wild, long-term captivity, or euthanasia) involves controversial decisions and complex ethical considerations. To navigate these issues, we developed an ethical matrix, an ethical framework previously used to address conservation-related issues. We gathered information from studies on the reintroduction of trafficked platyrrhines in Latin America from 1990 to 2022 to develop the matrix. We found 22 studies performed in eight Latin American countries, which included howler monkeys, spider monkeys, woolly monkeys, capuchin monkeys, squirrel monkeys, marmosets, and tamarins. We found that the reintroduction of trafficked platyrrhines may yield positive results for the welfare of individuals and for the conservation of their taxa and some of the potential negative effects, such as spillover of infectious agents to free-ranging populations or to human populations, or competition for resources between reintroduced monkeys and resident conspecifics have not yet been documented in the scientific literature, although this does not mean that they do not occur. We conclude that the ethical matrix is a useful method to consider the interests of all potential stakeholders and that the reintroduction of trafficked primates may be a viable management option if the individual welfare of the animals is considered, programs comply with the IUCN and government guidelines, and the objective and justification of the reintroduction are clear
From plane crashes to algorithmic harm: applicability of safety engineering frameworks for responsible ML
Inappropriate design and deployment of machine learning (ML) systems leads to
negative downstream social and ethical impact -- described here as social and
ethical risks -- for users, society and the environment. Despite the growing
need to regulate ML systems, current processes for assessing and mitigating
risks are disjointed and inconsistent. We interviewed 30 industry practitioners
on their current social and ethical risk management practices, and collected
their first reactions on adapting safety engineering frameworks into their
practice -- namely, System Theoretic Process Analysis (STPA) and Failure Mode
and Effects Analysis (FMEA). Our findings suggest STPA/FMEA can provide
appropriate structure toward social and ethical risk assessment and mitigation
processes. However, we also find nontrivial challenges in integrating such
frameworks in the fast-paced culture of the ML industry. We call on the ML
research community to strengthen existing frameworks and assess their efficacy,
ensuring that ML systems are safer for all people
Can Machines Learn Morality? The Delphi Experiment
As AI systems become increasingly powerful and pervasive, there are growing
concerns about machines' morality or a lack thereof. Yet, teaching morality to
machines is a formidable task, as morality remains among the most intensely
debated questions in humanity, let alone for AI. Existing AI systems deployed
to millions of users, however, are already making decisions loaded with moral
implications, which poses a seemingly impossible challenge: teaching machines
moral sense, while humanity continues to grapple with it.
To explore this challenge, we introduce Delphi, an experimental framework
based on deep neural networks trained directly to reason about descriptive
ethical judgments, e.g., "helping a friend" is generally good, while "helping a
friend spread fake news" is not. Empirical results shed novel insights on the
promises and limits of machine ethics; Delphi demonstrates strong
generalization capabilities in the face of novel ethical situations, while
off-the-shelf neural network models exhibit markedly poor judgment including
unjust biases, confirming the need for explicitly teaching machines moral
sense.
Yet, Delphi is not perfect, exhibiting susceptibility to pervasive biases and
inconsistencies. Despite that, we demonstrate positive use cases of imperfect
Delphi, including using it as a component model within other imperfect AI
systems. Importantly, we interpret the operationalization of Delphi in light of
prominent ethical theories, which leads us to important future research
questions
Abolish! Against the Use of Risk Assessment Algorithms at Sentencing in the US Criminal Justice System
In this article, I show why it is necessary to abolish the use of predictive algorithms in the US criminal justice system at sentencing. After presenting the functioning of these algorithms in their context of emergence, I offer three arguments to demonstrate why their abolition is imperative. First, I show that sentencing based on predictive algorithms induces a process of rewriting the temporality of the judged individual, flattening their life into a present inescapably doomed by its past. Second, I demonstrate that recursive processes, comprising predictive algorithms and the decisions based on their predictions, systematically suppress outliers and progressively transform reality to match predictions. In my third and final argument, I show that decisions made on the basis of predictive algorithms actively perform a biopolitical understanding of justice as management and modulation of risks. In such a framework, justice becomes a means to maintain a perverse social homeostasis that systematically exposes disenfranchised Black and Brown populations to risk