Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

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Artificial intelligence in radiology

A key goal of the fair-ML community is to develop machine-learning based systems that, once introduced into a social context, can achieve social and legal outcomes such as fairness, justice, and due process. Bedrock concepts in computer science---such as abstraction and modular design---are used to define notions of fairness and discrimination, to produce fairness-aware learning algorithms, and to intervene at different stages of a decision-making pipeline to produce "fair" outcomes. In this paper, however, we contend that these concepts render technical interventions ineffective, inaccurate, and sometimes dangerously misguided when they enter the societal context that surrounds decision-making systems. We outline this mismatch with five "traps" that fair-ML work can fall into even as it attempts to be more context-aware in comparison to traditional data science. We draw on studies of sociotechnical systems in Science and Technology Studies to explain why such traps occur and how to avoid them. Finally, we suggest ways in which technical designers can mitigate the traps through a refocusing of design in terms of process rather than solutions, and by drawing abstraction boundaries to include social actors rather than purely technical ones.

https://dl.acm.org/doi/pdf/10.1145/3287560.3287598

Fairness and Abstraction in Sociotechnical Systems

Summary 1. The xylem pressure inducing 50% loss of hydraulic conductivity due to embolism (P50 ) is widely used for comparisons of xylem vulnerability among species and across aridity gradients. However, despite its utility as an index of resistance to catastrophic xylem failure under extreme drought, P5o may have no special physiological relevance in the context of stomatal regulation of daily minimum xylem pressure and avoidance of hydraulic failure under non-extreme conditions. Moreover, few studies of hydraulic architecture have accounted for the buffering influence of tissue hydraulic capacitance on daily fluctuations in xylem pressure in intact plants. 2. We used data from 104 coniferous and angiosperm species representing a range of woody growth forms and habitat types to evaluate trends in three alternative xylem hydraulic safety margins based on features of their stem xylem vulnerability curves and regulation of daily minimum stem water potential (Y stem min ) under non-extreme conditions: (i)Ystem min - P50, (ii) Y stem min - Pe, the difference between Ystem min and the threshold xylem pressure at which loss of conductivity begins to increase rapidly (Pe) and (iii) Pe - P50 an estimate of the steepness of the vulnerability curve between Pe and P50. Additionally, we assessed relationships between xylem capacitance, species-specific set-points for daily minimum stem water potential and hydraulic safety margins in a subset of species for which relevant data were available. 3. The three types of hydraulic safety margin defined increased with decreasing species-specific set-points for Y stem min, suggesting a diminishing role of stem capacitance in slowing fluctuations in xylem pressure as Ystem min became more negative. The trends in hydraulic safety were similar among coniferous and angiosperm species native to diverse habitat types. 4. Our results suggest that here is a continuum of relative reliance on different mechanisms that confer hydraulic safety under dynamic conditions. Species with low capacitance and denser wood experience greater daily maximum xylem tension and appear to rely primarily on xylem structural features to avoid embolism, whereas in species with high capacitance and low wood density avoidance of embolism appears to be achieved primarily via reliance on transient release of stored water to constrain transpiration-induced fluctuations in xylem tension.

/pdf/xylem-hydraulic-safety-margins-in-woody-plants-coordination-4mjrxk0a00.pdf

Xylem hydraulic safety margins in woody plants: coordination of stomatal control of xylem tension with hydraulic capacitance

The potential for machine learning (ML) systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. A surge of recent work has focused on the development of algorithmic tools to assess and mitigate such unfairness. If these tools are to have a positive impact on industry practice, however, it is crucial that their design be informed by an understanding of real-world needs. Through 35 semi-structured interviews and an anonymous survey of 267 ML practitioners, we conduct the first systematic investigation of commercial product teams' challenges and needs for support in developing fairer ML systems. We identify areas of alignment and disconnect between the challenges faced by industry practitioners and solutions proposed in the fair ML research literature. Based on these findings, we highlight directions for future ML and HCI research that will better address industry practitioners' needs.

Improving fairness in machine learning systems: What do industry practitioners need?

Artificial intelligence in drug development: present status and future prospects

Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.

/pdf/google-deepmind-and-healthcare-in-an-age-of-algorithms-3s85sosokk.pdf

Google DeepMind and healthcare in an age of algorithms.

There is no single, neat statutory provision labeled the “right to explanation” in Europe’s new General Data Protection Regulation (GDPR). But nor is such a right illusory. 
Responding to two prominent papers that, in turn, conjure and critique the right to explanation in the context of automated decision-making, we advocate a return to the text of the GDPR. 
Articles 13-15 provide rights to “meaningful information about the logic involved” in automated decisions. This is a right to explanation, whether one uses the phrase or not. 
The right to explanation should be interpreted functionally, flexibly, and should, at a minimum, enable a data subject to exercise his or her rights under the GDPR and human rights law.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID3039125_code1328346.pdf?abstractid=3039125&mirid=1

Meaningful Information and the Right to Explanation

/pdf/meaningful-information-and-the-right-to-explanation-4l8c1hjv1u.pdf

Meaningful information and the right to explanation

To appear in the Proceedings of the 6th USENIX Workshop on Free and Open Communications on the Internet (FOCI 2016)

/pdf/ad-blocking-and-counter-blocking-a-slice-of-the-arms-race-55o4z1b50b.pdf

Ad-Blocking and Counter Blocking: A Slice of the Arms Race

Managing privacy in the IoT presents a significant challenge. We make the case that information obtained by auditing the flows of data can assist in demonstrating that the systems handling personal data satisfy regulatory and user requirements. Thus, components handling personal data should be audited to demonstrate that their actions comply with all such policies and requirements. A valuable side-effect of this approach is that such an auditing process will highlight areas where technical enforcement has been incompletely or incorrectly specified. There is a clear role for technical assistance in aligning privacy policy enforcement mechanisms with data protection regulations. The first step necessary in producing technology to accomplish this alignment is to gather evidence of data flows. We describe our work producing, representing and querying audit data and discuss outstanding challenges.

/pdf/data-provenance-to-audit-compliance-with-privacy-policy-in-2apgnib6l8.pdf

Julia Powles

Papers

Google DeepMind and healthcare in an age of algorithms.

Meaningful Information and the Right to Explanation

Meaningful information and the right to explanation

Ad-Blocking and Counter Blocking: A Slice of the Arms Race

Data provenance to audit compliance with privacy policy in the Internet of Things