Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions.Learn how toUse the scientific method to evaluate hypotheses using controlled experiments Define key metrics and ideally an Overall Evaluation CriterionTest for trustworthiness of the results and alert experimenters to violated assumptionsBuild a scalable platform that lowers the marginal cost of experiments close to zeroAvoid pitfalls like carryover effects and Twyman's lawUnderstand how statistical issues play out in practice.

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

This paper investigates incorporating community well-being metrics into the objectives of optimization algorithms and the teams that build them. It documents two cases where a large platform appears to have modified their system to this end. Facebook incorporated “well-being” metrics in 2017, while YouTube began integrating “user satisfaction” metrics around 2015. Metrics tied to community well-being outcomes could also be used in many other systems, such as a news recommendation system that tries to increase exposure to diverse views, or a product recommendation system that opstimizes for the carbon footprint of purchased products. Generalizing from these examples and incorporating insights from participatory design and AI governance leads to a proposed process for integrating community well-being into commercial AI systems: identify and involve the affected community, choose a useful metric, use this metric as a managerial performance measure and/or an algorithmic objective, and evaluate and adapt to outcomes. Important open questions include the best approach to community participation and the uncertain business effects of this process.

/pdf/aligning-ai-optimization-to-community-well-being-9r1e3yiu6o.pdf

Aligning AI Optimization to Community Well-Being.

The architecture, engineering, and construction (AEC) industry globally has a long history of prudently adopting novel technologies to improve products and services. Yet the rapid development of digital technology currently taking place is threatening to produce a more disruptive inflection, or substantial jolt. This paper explores the state of readiness of the AEC industry for such anticipated transformation. We illustrate our conceptual arguments with evidence from an explorative study across a sample of AEC organizations in Australia. At the core of this paper, we offer six provocations that highlight what we consider major challenges for the AEC industry—across multiple levels of analysis—related to the increasing role of digital technology. We then turn to lessons learned from other industries in order to propose a framework consisting of four leadership thinking schemas to enable digital transformation readiness: future thinking, strategic thinking, capability thinking, and experimental thinking. For these four schemas, we present practices and initiatives that may help AEC firms to better adapt—or to proactively create and shape a sustainable future. 

Digital Transformation in the Australian AEC Industry: Prevailing Issues and Prospective Leadership Thinking

Crafting job demands and employee creativity: A diary study

/pdf/ethics-owners-a-new-model-of-organizational-responsibility-3lgs4xx2t2.pdf

Ethics owners: a new model of organizational responsibility in data-driven technology companies

Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs

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Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs