49 research outputs found
Designing the Health-related Internet of Things: Ethical Principles and Guidelines
The conjunction of wireless computing, ubiquitous Internet access, and the miniaturisation of sensors have opened the door for technological applications that can monitor health and well-being outside of formal healthcare systems. The health-related Internet of Things (H-IoT) increasingly plays a key role in health management by providing real-time tele-monitoring of patients, testing of treatments, actuation of medical devices, and fitness and well-being monitoring. Given its numerous applications and proposed benefits, adoption by medical and social care institutions and consumers may be rapid. However, a host of ethical concerns are also raised that must be addressed. The inherent sensitivity of health-related data being generated and latent risks of Internet-enabled devices pose serious challenges. Users, already in a vulnerable position as patients, face a seemingly impossible task to retain control over their data due to the scale, scope and complexity of systems that create, aggregate, and analyse personal health data. In response, the H-IoT must be designed to be technologically robust and scientifically reliable, while also remaining ethically responsible, trustworthy, and respectful of user rights and interests. To assist developers of the H-IoT, this paper describes nine principles and nine guidelines for ethical design of H-IoT devices and data protocols
The Ethical Implications of Personal Health Monitoring
Personal Health Monitoring (PHM) uses electronic devices which monitor and record health-related data outside a hospital, usually within the home. This paper examines the ethical issues raised by PHM. Eight themes describing the ethical implications of PHM are identified through a review of 68 academic articles concerning PHM. The identified themes include privacy, autonomy, obtrusiveness and visibility, stigma and identity, medicalisation, social isolation, delivery of care, and safety and technological need. The issues around each of these are discussed. The system / lifeworld perspective of Habermas is applied to develop an understanding of the role of PHMs as mediators of communication between the institutional and the domestic environment. Furthermore, links are established between the ethical issues to demonstrate that the ethics of PHM involves a complex network of ethical interactions. The paper extends the discussion of the critical effect PHMs have on the patient’s identity and concludes that a holistic understanding of the ethical issues surrounding PHMs will help both researchers and practitioners in developing effective PHM implementations
Explaining Explanations in AI
Recent work on interpretability in machine learning and AI has focused on the building of simplified models that approximate the true criteria used to make decisions. These models are a useful pedagogical device for teaching trained professionals how to predict what decisions will be made by the complex system, and most importantly how the system might break. However, when considering any such model it’s important to remember Box’s maxim that "All models are wrong but some are useful." We focus on the distinction between these models and explanations in philosophy and sociology. These models can be understood as a "do it yourself kit" for explanations, allowing a practitioner to directly answer "what if questions" or generate contrastive explanations without external assistance. Although a valuable ability, giving these models as explanations appears more difficult than necessary, and other forms of explanation may not have the same trade-offs. We contrast the different schools of thought on what makes an explanation, and suggest that machine learning might benefit from viewing the problem more broadly
Principles alone cannot guarantee ethical AI
AI Ethics is now a global topic of discussion in academic and policy circles.
At least 84 public-private initiatives have produced statements describing
high-level principles, values, and other tenets to guide the ethical
development, deployment, and governance of AI. According to recent
meta-analyses, AI Ethics has seemingly converged on a set of principles that
closely resemble the four classic principles of medical ethics. Despite the
initial credibility granted to a principled approach to AI Ethics by the
connection to principles in medical ethics, there are reasons to be concerned
about its future impact on AI development and governance. Significant
differences exist between medicine and AI development that suggest a principled
approach in the latter may not enjoy success comparable to the former. Compared
to medicine, AI development lacks (1) common aims and fiduciary duties, (2)
professional history and norms, (3) proven methods to translate principles into
practice, and (4) robust legal and professional accountability mechanisms.
These differences suggest we should not yet celebrate consensus around
high-level principles that hide deep political and normative disagreement.Comment: A previous, pre-print version of this paper was entitled 'AI Ethics -
Too Principled to Fail?
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
There has been much discussion of the right to explanation in the EU General
Data Protection Regulation, and its existence, merits, and disadvantages.
Implementing a right to explanation that opens the black box of algorithmic
decision-making faces major legal and technical barriers. Explaining the
functionality of complex algorithmic decision-making systems and their
rationale in specific cases is a technically challenging problem. Some
explanations may offer little meaningful information to data subjects, raising
questions around their value. Explanations of automated decisions need not
hinge on the general public understanding how algorithmic systems function.
Even though such interpretability is of great importance and should be pursued,
explanations can, in principle, be offered without opening the black box.
Looking at explanations as a means to help a data subject act rather than
merely understand, one could gauge the scope and content of explanations
according to the specific goal or action they are intended to support. From the
perspective of individuals affected by automated decision-making, we propose
three aims for explanations: (1) to inform and help the individual understand
why a particular decision was reached, (2) to provide grounds to contest the
decision if the outcome is undesired, and (3) to understand what would need to
change in order to receive a desired result in the future, based on the current
decision-making model. We assess how each of these goals finds support in the
GDPR. We suggest data controllers should offer a particular type of
explanation, unconditional counterfactual explanations, to support these three
aims. These counterfactual explanations describe the smallest change to the
world that can be made to obtain a desirable outcome, or to arrive at the
closest possible world, without needing to explain the internal logic of the
system
Transparent, explainable, and accountable AI for robotics
To create fair and accountable AI and robotics, we need precise regulation and better methods to certify, explain, and audit inscrutable systems
The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices
The technical progression of artificial intelligence (AI) research has been
built on breakthroughs in fields such as computer science, statistics, and
mathematics. However, in the past decade AI researchers have increasingly
looked to the social sciences, turning to human interactions to solve the
challenges of model development. Paying crowdsourcing workers to generate or
curate data, or data enrichment, has become indispensable for many areas of AI
research, from natural language processing to reinforcement learning from human
feedback (RLHF). Other fields that routinely interact with crowdsourcing
workers, such as Psychology, have developed common governance requirements and
norms to ensure research is undertaken ethically. This study explores how, and
to what extent, comparable research ethics requirements and norms have
developed for AI research and data enrichment. We focus on the approach taken
by two leading conferences: ICLR and NeurIPS, and journal publisher Springer.
In a longitudinal study of accepted papers, and via a comparison with
Psychology and CHI papers, this work finds that leading AI venues have begun to
establish protocols for human data collection, but these are are inconsistently
followed by authors. Whilst Psychology papers engaging with crowdsourcing
workers frequently disclose ethics reviews, payment data, demographic data and
other information, similar disclosures are far less common in leading AI venues
despite similar guidance. The work concludes with hypotheses to explain these
gaps in research ethics practices and considerations for its implications.Comment: 10 page
On the Ethical Implications of Personal Health Monitoring
Recent years have seen an influx of medical technologies capable of remotely monitoring the health and behaviours of individuals to detect, manage and prevent health problems. Known collectively as personal health monitoring (PHM), these systems are intended to supplement medical care with health monitoring outside traditional care environments such as hospitals, ranging in complexity from mobile devices to complex networks of sensors measuring physiological parameters and behaviours. This research project assesses the potential ethical implications of PHM as an emerging medical technology, amenable to anticipatory action intended to prevent or mitigate problematic ethical issues in the future.
PHM fundamentally changes how medical care can be delivered: patients can be monitored and consulted at a distance, eliminating opportunities for face-to-face actions and potentially undermining the importance of social, emotional and psychological aspects of medical care. The norms evident in this movement may clash with existing standards of ‘good’ medical practice from the perspective of patients, clinicians and institutions. By relating utilitarianism, virtue ethics and theories of surveillance to Habermas’ concept of colonisation of the lifeworld, a conceptual framework is created which can explain how PHM may be allowed to change medicine as a practice in an ethically problematic way. The framework relates the inhibition of virtuous behaviour among practitioners of medicine, understood as a moral practice, to the movement in medicine towards remote monitoring.
To assess the explanatory power of the conceptual framework and expand its borders, a qualitative interview empirical study with potential users of PHM in England is carried out. Recognising that the inherent uncertainty of the future undermines the validity of empirical research, a novel epistemological framework based in Habermas’ discourse ethics is created to justify the empirical study. By developing Habermas’ concept of translation into a procedure for assessing the credibility of uncertain normative claims about the future, a novel methodology for empirical ethical assessment of emerging technologies is created and tested. Various methods of analysis are employed, including review of academic discourses, empirical and theoretical analyses of the moral potential of PHM. Recommendations are made concerning ethical issues in the deployment and design of PHM systems, analysis and application of PHM data, and the shortcomings of existing research and protection mechanisms in responding to potential ethical implications of the technology.he research described in this thesis was sponsored and funded by the Centre for Computing and Social Responsibility of De Montfort University, and was linked to the research carried out in FP7 research projects PHM-Ethics (GA 230602) and ETICA (Ethical Issues of Emerging ICT Applications, GA 230318)
The Unfairness of Fair Machine Learning: Levelling down and strict egalitarianism by default
In recent years fairness in machine learning (ML) has emerged as a highly
active area of research and development. Most define fairness in simple terms,
where fairness means reducing gaps in performance or outcomes between
demographic groups while preserving as much of the accuracy of the original
system as possible. This oversimplification of equality through fairness
measures is troubling. Many current fairness measures suffer from both fairness
and performance degradation, or "levelling down," where fairness is achieved by
making every group worse off, or by bringing better performing groups down to
the level of the worst off. When fairness can only be achieved by making
everyone worse off in material or relational terms through injuries of stigma,
loss of solidarity, unequal concern, and missed opportunities for substantive
equality, something would appear to have gone wrong in translating the vague
concept of 'fairness' into practice. This paper examines the causes and
prevalence of levelling down across fairML, and explore possible justifications
and criticisms based on philosophical and legal theories of equality and
distributive justice, as well as equality law jurisprudence. We find that
fairML does not currently engage in the type of measurement, reporting, or
analysis necessary to justify levelling down in practice. We propose a first
step towards substantive equality in fairML: "levelling up" systems by design
through enforcement of minimum acceptable harm thresholds, or "minimum rate
constraints," as fairness constraints. We likewise propose an alternative
harms-based framework to counter the oversimplified egalitarian framing
currently dominant in the field and push future discussion more towards
substantive equality opportunities and away from strict egalitarianism by
default. N.B. Shortened abstract, see paper for full abstract
Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI
This article identifies a critical incompatibility between European notions
of discrimination and existing statistical measures of fairness. First, we
review the evidential requirements to bring a claim under EU non-discrimination
law. Due to the disparate nature of algorithmic and human discrimination, the
EU's current requirements are too contextual, reliant on intuition, and open to
judicial interpretation to be automated. Second, we show how the legal
protection offered by non-discrimination law is challenged when AI, not humans,
discriminate. Humans discriminate due to negative attitudes (e.g. stereotypes,
prejudice) and unintentional biases (e.g. organisational practices or
internalised stereotypes) which can act as a signal to victims that
discrimination has occurred. Finally, we examine how existing work on fairness
in machine learning lines up with procedures for assessing cases under EU
non-discrimination law. We propose "conditional demographic disparity" (CDD) as
a standard baseline statistical measurement that aligns with the European Court
of Justice's "gold standard." Establishing a standard set of statistical
evidence for automated discrimination cases can help ensure consistent
procedures for assessment, but not judicial interpretation, of cases involving
AI and automated systems. Through this proposal for procedural regularity in
the identification and assessment of automated discrimination, we clarify how
to build considerations of fairness into automated systems as far as possible
while still respecting and enabling the contextual approach to judicial
interpretation practiced under EU non-discrimination law.
N.B. Abridged abstrac