Research
Aviad Raz, Bert Heinrichs, Netta Avnoon, Gil Eyal, Yael Inbar
The debate regarding prediction and explainability in artificial intelligence (AI) centers around the trade-off between achieving high-performance accurate models and the ability to understand and interpret the decisionmaking process of those models. In recent years, this debate has gained significant attention due to the increasing adoption of AI systems in various domains, including healthcare, finance, and criminal justice. While prediction and explainability are desirable goals in principle, the recent spread of high accuracy yet opaque machine learning (ML) algorithms has highlighted the trade-off between the two, marking this debate as an inter-disciplinary, inter-professional arena for negotiating expertise. There is no longer an agreement about what should be the “default” balance of prediction and explainability, with various positions reflecting claims for professional jurisdiction. Overall, there appears to be a growing schism between the regulatory and ethics-based call for explainability as a condition for trustworthy AI, and how it is being designed, assimilated, and negotiated. The impetus for writing this commentary comes from recent suggestions that explainability is overrated, including the argument that explainability is not guaranteed in human healthcare experts either. To shed light on this debate, its premises, and its recent twists, we provide an overview of key arguments representing different frames, focusing on AI in healthcare.
Sagit Bar-Gill, Yael Inbar, Shachar Reichman
The digitization of news markets has created a key role for online referring channels. This research combines field and lab experiments, and analysis of large-scale clickstream data, to study the effects of social versus non-social referral sources on news consumption in a referred news website visit. We propose that referring channels generate a new type of priming effect, denoted the referrer effect, as unique features of the referrer affect user behavior in a subsequent news visit. We find that social media referrals promote focused reading – visits with fewer articles, shorter durations, yet higher reading completion rates - compared to non-social referrals. Furthermore, social referrals decrease news sharing propensity, due to lower perceived novelty to peers of content discovered via social media. The results provide insights applicable to news outlets’ social media strategies, and speak to ongoing debates regarding biases arising from social media’s growing importance as an avenue for news consumption.
David Zvilichovsky, Yael Inbar, Ohad Barzilay
Crowdfunding platforms constitute two-sided markets, bringing together entrepreneurs and potential backers; this peer-based fundraising schema introduces new dynamics into the fundraising process. We focus on platform agents who play on both sides of the market, supporting the ventures of their fellow entrepreneurs and subsequently raising money for their own venture. Acting on both sides of the market is a peer-economy phenomenon that has not yet received much attention. We find that an entrepreneur’s backing-history has a significant effect on financing outcomes; campaigns initiated by entrepreneurs who have previously supported others have higher success rates, attract more backers and collect more funds. We find evidence that supports the existence of a causal channel from playing both sides of the market to increased crowdfunding success. We also provide evidence as to the existence of reciprocity. Project owners back their backers, when possible, at a rate that is significantly higher than other comparable projects. We estimate the effect of such actions on the performance of crowdfunding campaigns and show that playing both sides of the market is a rewarding strategy.