Paradoxes in Fair Computer-Aided Decision Making

Computer-aided decision making--where a human decision-maker is aided by a computational classifier in making a decision--is becoming increasingly prevalent. For instance, judges in at least nine states make use of algorithmic tools meant to determine "recidivism risk scores" for criminal defendants in sentencing, parole, or bail decisions. A subject of much recent debate is whether such algorithmic tools are "fair" in the sense that they do not discriminate against certain groups (e.g., races) of people. Our main result shows that for "non-trivial" computer-aided decision making, either the classifier must be discriminatory, or a rational decision-maker using the output of the classifier is forced to be discriminatory. We further provide a complete characterization of situations where fair computer-aided decision making is possible

Estimated clinical outcomes and cost-effectiveness associated with provision of addiction treatment in US primary care clinics

IMPORTANCE: US primary care practitioners (PCPs) are the largest clinical workforce, but few provide addiction care. Primary care is a practical place to expand addiction services, including buprenorphine and harm reduction kits, yet the clinical outcomes and health care sector costs are unknown. OBJECTIVE: To estimate the long-term clinical outcomes, costs, and cost-effectiveness of integrated buprenorphine and harm reduction kits in primary care for people who inject opioids. DESIGN, SETTING, AND PARTICIPANTS: In this modeling study, the Reducing Infections Related to Drug Use Cost-Effectiveness (REDUCE) microsimulation model, which tracks serious injection-related infections, overdose, hospitalization, and death, was used to examine the following treatment strategies: (1) PCP services with external referral to addiction care (status quo), (2) PCP services plus onsite buprenorphine prescribing with referral to offsite harm reduction kits (BUP), and (3) PCP services plus onsite buprenorphine prescribing and harm reduction kits (BUP plus HR). Model inputs were derived from clinical trials and observational cohorts, and costs were discounted annually at 3%. The cost-effectiveness was evaluated over a lifetime from the modified health care sector perspective, and sensitivity analyses were performed to address uncertainty. Model simulation began January 1, 2021, and ran for the entire lifetime of the cohort. MAIN OUTCOMES AND MEASURES: Life-years (LYs), hospitalizations, mortality from sequelae (overdose, severe skin and soft tissue infections, and endocarditis), costs, and incremental cost-effectiveness ratios (ICERs). RESULTS: The simulated cohort included 2.25 million people and reflected the age and gender of US persons who inject opioids. Status quo resulted in 6.56 discounted LYs at a discounted cost of $203 500 per person (95$203 000-$222 000). Each strategy extended discounted life expectancy: BUP by 0.16 years and BUP plus HR by 0.17 years. Compared with status quo, BUP plus HR reduced sequelae-related mortality by 33$34 400 per LY). During a 5-year time horizon, BUP plus HR cost an individual PCP practice approximately $13 000. CONCLUSIONS AND RELEVANCE: This modeling study of integrated addiction service in primary care found improved clinical outcomes and modestly increased costs. The integration of addiction service into primary care practices should be a health care system priority

Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causality

As virtually all aspects of our lives are increasingly impacted by algorithmic decision making systems, it is incumbent upon us as a society to ensure such systems do not become instruments of unfair discrimination on the basis of gender, race, ethnicity, religion, etc. We consider the problem of determining whether the decisions made by such systems are discriminatory, through the lens of causal models. We introduce two definitions of group fairness grounded in causality: fair on average causal effect (FACE), and fair on average causal effect on the treated (FACT). We use the Rubin-Neyman potential outcomes framework for the analysis of cause-effect relationships to robustly estimate FACE and FACT. We demonstrate the effectiveness of our proposed approach on synthetic data. Our analyses of two real-world data sets, the Adult income data set from the UCI repository (with gender as the protected attribute), and the NYC Stop and Frisk data set (with race as the protected attribute), show that the evidence of discrimination obtained by FACE and FACT, or lack thereof, is often in agreement with the findings from other studies. We further show that FACT, being somewhat more nuanced compared to FACE, can yield findings of discrimination that differ from those obtained using FACE.Comment: 7 pages, 2 figures, 2 tables.To appear in Proceedings of the International Conference on World Wide Web (WWW), 201

Datatrust: Or, the political quest for numerical evidence and the epistemologies of Big Data

Recently, there has been renewed interest in so-called evidence-based policy making. Enticed by the grand promises of Big Data, public officials seem increasingly inclined to experiment with more data-driven forms of governance. But while the rise of Big Data and related consequences has been a major issue of concern across different disciplines, attempts to develop a better understanding of the phenomenon's historical foundations have been rare. This short commentary addresses this gap by situating the current push for numerical evidence within a broader socio-political context, demonstrating how the epistemological claims of Big Data science intersect with specific forms of trust, truth, and objectivity. We conclude by arguing that regulators' faith in numbers can be attributed to a distinct political culture, a representative democracy undermined by pervasive public distrust and uncertainty

Systematic review and economic modelling of the relative clinical benefit and cost-effectiveness of laparoscopic surgery and robotic surgery for removal of the prostate in men with localised prostate cancer

PMID: 23127367 [PubMed - indexed for MEDLINE] Free full text Funding for this study was provided by the Health Technology Assessment programme of the National Institute for Health Research.Peer reviewedPublisher PD

Elastic interactions of active cells with soft materials

Anchorage-dependent cells collect information on the mechanical properties of the environment through their contractile machineries and use this information to position and orient themselves. Since the probing process is anisotropic, cellular force patterns during active mechanosensing can be modelled as anisotropic force contraction dipoles. Their build-up depends on the mechanical properties of the environment, including elastic rigidity and prestrain. In a finite sized sample, it also depends on sample geometry and boundary conditions through image strain fields. We discuss the interactions of active cells with an elastic environment and compare it to the case of physical force dipoles. Despite marked differences, both cases can be described in the same theoretical framework. We exactly solve the elastic equations for anisotropic force contraction dipoles in different geometries (full space, halfspace and sphere) and with different boundary conditions. These results are then used to predict optimal position and orientation of mechanosensing cells in soft material.Comment: Revtex, 38 pages, 8 Postscript files included; revised version, accepted for publication in Phys. Rev.

The ethics of uncertainty for data subjects

Modern health data practices come with many practical uncertainties. In this paper, I argue that data subjects’ trust in the institutions and organizations that control their data, and their ability to know their own moral obligations in relation to their data, are undermined by significant uncertainties regarding the what, how, and who of mass data collection and analysis. I conclude by considering how proposals for managing situations of high uncertainty might be applied to this problem. These emphasize increasing organizational flexibility, knowledge, and capacity, and reducing hazard

Concomitant CIS on TURBT does not impact oncological outcomes in patients treated with neoadjuvant or induction chemotherapy followed by radical cystectomy

© Springer-Verlag GmbH Germany, part of Springer Nature 2018Background: Cisplatin-based neoadjuvant chemotherapy (NAC) for muscle invasive bladder cancer improves all-cause and cancer specific survival. We aimed to evaluate whether the detection of carcinoma in situ (CIS) at the time of initial transurethral resection of bladder tumor (TURBT) has an oncological impact on the response to NAC prior to radical cystectomy. Patients and methods: Patients were identified retrospectively from 19 centers who received at least three cycles of NAC or induction chemotherapy for cT2-T4aN0-3M0 urothelial carcinoma of the bladder followed by radical cystectomy between 2000 and 2013. The primary and secondary outcomes were pathological response and overall survival, respectively. Multivariable analysis was performed to determine the independent predictive value of CIS on these outcomes. Results: Of 1213 patients included in the analysis, 21.8% had concomitant CIS. Baseline clinical and pathologic characteristics of the ‘CIS’ versus ‘no-CIS’ groups were similar. The pathological response did not differ between the two arms when response was defined as pT0N0 (17.9% with CIS vs 21.9% without CIS; p = 0.16) which may indicate that patients with CIS may be less sensitive to NAC or ≤ pT1N0 (42.8% with CIS vs 37.8% without CIS; p = 0.15). On Cox regression model for overall survival for the cN0 cohort, the presence of CIS was not associated with survival (HR 0.86 (95% CI 0.63–1.18; p = 0.35). The presence of LVI (HR 1.41, 95% CI 1.01–1.96; p = 0.04), hydronephrosis (HR 1.63, 95% CI 1.23–2.16; p = 0.001) and use of chemotherapy other than ddMVAC (HR 0.57, 95% CI 0.34–0.94; p = 0.03) were associated with shorter overall survival. For the whole cohort, the presence of CIS was also not associated with survival (HR 1.05 (95% CI 0.82–1.35; p = 0.70). Conclusion: In this multicenter, real-world cohort, CIS status at TURBT did not affect pathologic response to neoadjuvant or induction chemotherapy. This study is limited by its retrospective nature as well as variability in chemotherapy regimens and surveillance regimens.Peer reviewedFinal Accepted Versio

Governing Artificial Intelligence to benefit the UN Sustainable Development Goals

Big Tech's unregulated roll-out out of experimental AI poses risks to the achievement of the UN Sustainable Development Goals (SDGs), with particular vulnerability for developing countries. The goal of financial inclusion is threatened by the imperfect and ungoverned design and implementation of AI decision-making software making important financial decisions affecting customers. Automated decision-making algorithms have displayed evidence of bias, lack ethical governance, and limit transparency in the basis for their decisions, causing unfair outcomes and amplify unequal access to finance. Poverty reduction and sustainable development targets are risked by Big Tech's potential exploitation of developing countries by using AI to harvest data and profits. Stakeholder progress toward preventing financial crime and corruption is further threatened by potential misuse of AI. In the light of such risks, Big Tech's unscrupulous history means it cannot be trusted to operate without regulatory oversight. The article proposes effective pre-emptive regulatory options to minimize scenarios of AI damaging the SDGs. It explores internationally accepted principles of AI governance, and argues for their implementation as regulatory requirements governing AI developers and coders, with compliance verified through algorithmic auditing. Furthermore, it argues that AI governance frameworks must require a benefit to the SDGs. The article argues that proactively predicting such problems can enable continued AI innovation through well-designed regulations adhering to international principles. It highlights risks of unregulated AI causing harm to human interests, where a public and regulatory backlash may result in over-regulation that could damage the otherwise beneficial development of AI.Qatar National Research Fund, Grant/Award Number: NPRP 11S-1119-17001

Establishing a global quality of care benchmark report.

BACKGROUND: The Movember funded TrueNTH Global Registry (TNGR) aims to improve care by collecting and analysing a consistent dataset to identify variation in disease management, benchmark care delivery in accordance with best practice guidelines and provide this information to those in a position to enact change. We discuss considerations of designing and implementing a quality of care report for TNGR. METHODS: Eleven working group sessions were held prior to and as reports were being built with representation from clinicians, data managers and investigators contributing to TNGR. The aim of the meetings was to understand current data display approaches, share literature review findings and ideas for innovative approaches. Preferred displays were evaluated with two surveys (survey 1: 5 clinicians and 5 non-clinicians, 83% response rate; survey 2: 17 clinicians and 18 non-clinicians, 93% response rate). RESULTS: Consensus on dashboard design and three data-display preferences were achieved. The dashboard comprised two performance summary charts; one summarising site's relative quality indicator (QI) performance and another to summarise data quality. Binary outcome QIs were presented as funnel plots. Patient-reported outcome measures of function score and the extent to which men were bothered by their symptoms were presented in bubble plots. Time series graphs were seen as providing important information to supplement funnel and bubble plots. R Markdown was selected as the software program principally because of its excellent analytic and graph display capacity, open source licensing model and the large global community sharing program code enhancements. CONCLUSIONS: International collaboration in creating and maintaining clinical quality registries has allowed benchmarking of process and outcome measures on a large scale. A registry report system was developed with stakeholder engagement to produce dynamic reports that provide user-specific feedback to 132 participating sites across 13 countries