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

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.Comment: To appear in the 2019 ACM CHI Conference on Human Factors in Computing Systems (CHI 2019

Increased survival of non low-grade and deep-seated soft tissue sarcoma after surgical management in high-volume hospitals : a nationwide study from the Netherlands

BACKGROUND: Diagnosing and treating soft tissue sarcomas (STSs) remains challenging, stressing the urgency for centralisation. This nationwide survey aimed to evaluate the centralisation of STS surgery and its effect on survival. METHODS: Patients operated for primary STS from 2006 to 2015 were queried from the Netherlands Cancer Registry. Hospitals in which STS surgery was performed were allocated into three categories: low-volume (1-9 resections per year), medium-volume (10-19 resections) or high-volume (≥20 resections). Differences in tumour characteristics and outcome were calculated. A multivariable regression analysis was performed to adjust for case-mix. RESULTS: Of the 5282 identified patients, 42% was treated in low-volume hospitals, 7.7% in medium-volume hospitals and 51% in high-volume hospitals, with a significant trend over time towards treatment in a high-volume hospital (p < 0.01). In high-volume hospitals, more often patients with non low-grade, large and deep-seated tumours were treated than in low-volume hospitals. For the whole group, there was no survival benefit for patients treated in high-volume hospitals, with 10-year net survival rates of 76% (low-volume), 68% (medium-volume) and 68% (high-volume). However, subgroup analysis for patients with non low-grade and deep-seated tumours did reveal a benefit from treatment in a high-volume hospitals with 10-year survival rates of 54% (high-volume), 49% (low-volume) and 42% (medium-volume) and a relative risk of 1.3 (high-volume versus low-volume, p = 0.03). CONCLUSION: Centralisation of STS surgery has increased in the past decade. Surgery in a high-volume hospital improved survival of patients with non low-grade and deep-seated tumours, and therefore these patients should be referred to such a hospital