BEGIN:VCALENDAR VERSION:2.0 X-WR-CALNAME:EventsCalendar PRODID:-//hacksw/handcal//NONSGML v1.0//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York LAST-MODIFIED:20240422T053451Z TZURL:https://www.tzurl.org/zoneinfo-outlook/America/New_York X-LIC-LOCATION:America/New_York BEGIN:DAYLIGHT TZNAME:EDT TZOFFSETFROM:-0500 TZOFFSETTO:-0400 DTSTART:19700308T020000 RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=2SU END:DAYLIGHT BEGIN:STANDARD TZNAME:EST TZOFFSETFROM:-0400 TZOFFSETTO:-0500 DTSTART:19701101T020000 RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU END:STANDARD END:VTIMEZONE BEGIN:VEVENT CATEGORIES:Charlton College of Business DESCRIPTION:The Department of Accounting & Finance announces the following research seminar. Speaker: Professor of Business Administration, Charles C .Y. Wang (Harvard Business School) Title: The Governance of Algorithmic Di sclosures: A Black-Box Audit for Incentive-Aligned Distortion Date: Friday , October 16, 2026Time: 9:30 -10:45 AMLocation: via Zoom MeetingMeeting ID : 438 755 1509 Abstract: Algorithmic disclosures, the signals that algorit hms generate to inform consumer decisions, increasingly shape how people s pend their time and money. Yet their accuracy is rarely verified by anyone outside the platform that produces them. We propose a framework for audit ing them without access to the underlying algorithm or its data. In a simp le model, a platform generates a signal about a product attribute to influ ence a consumer’s transaction decision. The model predicts that distorti on increases with the platform’s conversion value and the strength of th e consumer’s outside option, and decreases with detection cost. In contr ast, disclosures with little influence on conversion exhibit little incent ive-aligned distortion. This suggests a black-box audit: identify the disc losures likely tied to the platform’s incentives, and, where available, lower-stakes comparison disclosures; compare those disclosures with indepe ndently measured outcome benchmarks across conditions that capture variati on in distortion incentives. We apply this approach to one of the largest ride-hailing platforms in Hong Kong through a black-box field audit in whi ch seven trained mystery riders, each using a dedicated account, generated 515 completed rides under a common protocol. Platform-recorded pick-up du rations exceed the displayed estimates by 55.4% on average, compared to 6. 8% for travel time. Consistent with incentive-aligned distortion, we find that pick-up underestimation is more severe for routes near mass transit s ubstitutes and during peak hours and less severe for attentive riders; tra vel-time estimates show little corresponding variation. We discuss other a pplications and what an audit regime for algorithmic disclosures would req uire. For additional information, please contact Prof. Hongkang Xu at hxu5 @umassd.edu.\nEvent page: https://www.umassd.edu/events/cms/10-16-26-accou nting-and-finance-department-research-seminar.php\nEvent link: https://uma ssd.zoom.us/j/4387551509?pwd=bVB2QzdtRmFCdkk1WTVQSUxHMS9iQT09&omn=98524562 508 X-ALT-DESC;FMTTYPE=text/html:

The Department of Accounting & Finance announces the following research seminar.

Speaker: Profess or of Business Administration\, Charles C.Y. Wang (Harvard Business School )

Title: The Governance of Algorithmic Disclosures: A Black-Box Au dit for Incentive-Aligned Distortion

Date: Friday\, October 16\, 2 026
Time: 9:30 -10:45 AM
Location: via Zoom Meeting
Meeting ID: 4 38 755 1509

Abstract:

Algorithmic disclosures\, the signal s that algorithms generate to inform consumer decisions\, increasingly sha pe how people spend their time and money. Yet their accuracy is rarely ver ified by anyone outside the platform that produces them. We propose a fram ework for auditing them without access to the underlying algorithm or its data. In a simple model\, a platform generates a signal about a product at tribute to influence a consumer’s transaction decision. The model predic ts that distortion increases with the platform’s conversion value and th e strength of the consumer’s outside option\, and decreases with detecti on cost. In contrast\, disclosures with little influence on conversion exh ibit little incentive-aligned distortion. This suggests a black-box audit: identify the disclosures likely tied to the platform’s incentives\, and \, where available\, lower-stakes comparison disclosures\; compare those d isclosures with independently measured outcome benchmarks across condition s that capture variation in distortion incentives. We apply this approach to one of the largest ride-hailing platforms in Hong Kong through a black- box field audit in which seven trained mystery riders\, each using a dedic ated account\, generated 515 completed rides under a common protocol. Plat form-recorded pick-up durations exceed the displayed estimates by 55.4% on average\, compared to 6.8% for travel time. Consistent with incentive-ali gned distortion\, we find that pick-up underestimation is more severe for routes near mass transit substitutes and during peak hours and less severe for attentive riders\; travel-time estimates show little corresponding va riation. We discuss other applications and what an audit regime for algori thmic disclosures would require.

For additional information\, plea se contact Prof. Hongkang Xu at hxu5@umassd.edu.

Event page: https://www.umassd.edu/events/cms/10-16-26-acco unting-and-finance-department-research-seminar.php
Event link: https://umassd.zoom.us/j/4387551509?pwd=bVB2QzdtRmFCd kk1WTVQSUxHMS9iQT09&\;omn=98524562508

DTSTAMP:20261002T154436 DTSTART;TZID=America/New_York:20261016T093000 DTEND;TZID=America/New_York:20261016T104500 LOCATION:zoom SUMMARY;LANGUAGE=en-us:Accounting and Finance Department Research Seminar UID:db93d94f26b2f90e2da442f8e1cdce50@www.umassd.edu END:VEVENT END:VCALENDAR