• Evaluating alternative remedies for proven violations of competition law.
• Online forensics to determine whether a given publisher/partner is sending legitimate traffic versus malware, invisible traffic, fake clicks, and the like.
• Measuring the incrementality of ad campaigns to distinguish the genuine incremental benefit, versus the sales that would have occurred anyway.
• Estimating the market value of IPv4 addresses, and evaluating the impact of rules and restrictions on their transfer.
• Exploring Excel data glitches including how data can become corrupted inadvertently, and what can be learned from internal Excel data structures.

I’m enjoying combining software engineering, law, economics, and (often) a bit of gumshoe work.  And it’s a delight to always be learning!

Last month I began to serve as an advisor at Geradin Partners, a European law firm best known for its leadership in matters adverse to big tech.  Fully two decades ago, I was already flagging tensions between Main Street and Silicon Valley.  Those ideas took off slowly in the US, but in Europe they moved faster, in no small part thanks to the attorneys now at Geradin Partners.  I’m looking forward to working with them, and their clients, for all manner of projects with a locus in Europe.

More announcements to follow as to other affiliations.

Status: Briefing underway.

Summary: In prominent marketing offers, including onboard napkins and large airport displays, AA promises “first checked bag free” if customers get certain AA-partner credit cards.  But AA denies that benefit on itineraries that are completely or partially international — a restriction nowhere mentioned in initial marketing offers.

First, we asked 77 native Japanese speakers to review a meeting recorded in English. Half the participants had to watch and listen to the video. The other half could use Copilot Meeting Recap, which gave them an AI meeting summary as well as a chatbot to answer questions about the meeting.

Then, we asked 83 other native Japanese speakers to review a similar meeting, following the same script, but this time held in Japanese by native Japanese speakers. Again, half of participants had access to Copilot.

For the meeting in English, participants with Copilot answered 16.4% more multiple-choice questions about the meeting correctly, and they were more than twice as likely to get a perfect score.  Moreover, in comparing accuracy between the two scenarios, people listening to a meeting in English with Copilot achieved 97.5% accuracy, slightly more accurate than people listening to a meeting in their native Japanese using standard tools (94.8%). This is a statistically significant difference (p<.05). The changes are small in percentage point terms because the baseline accuracy is so high, but Copilot closed 38.5% of the gap to perfect accuracy for those working in their native language (p<0.10) and closed 84.6% of the gap for those working in (non-native) English (p<.05).

Summary from Jaffe et al, Generative AI in Real-World Workplaces, July 2024.

Recently, we ran a controlled experiment with Microsoft’s Office of the Chief Economist, and the results are groundbreaking. In this experiment, we asked legal professional volunteers on our team to complete three realistic legal tasks and randomly granted Copilot to some participants. Individuals with Copilot completed the tasks 32% faster and with 20.3% greater accuracy!

Copilot isn’t just a tool; it’s a game-changer, empowering our team to focus on what truly matters by enhancing productivity, elevating work quality, and, most importantly, reclaiming time.

All findings statistically significant at P<0.05.

Full results.

Status: Pending

Summary: AA delayed an intercontinental flight by 27 hours, but in email notification didn’t say what flight was delayed or by how long.  AA’s staff offered contradictory statements of passenger rights and rebooking options, including multiple supposed rules untethered to the Tariff.

This report presents the initial findings of Microsoft’s research initiative on “AI and Productivity”, which seeks to measure and accelerate the productivity gains created by LLM-powered productivity tools like Microsoft’s Copilot. The many studies summarized in this report, the initiative’s first, focus on common enterprise information worker tasks for which LLMs are most likely to provide significant value. Results from the studies support the hypothesis that the first versions of Copilot tools substantially increase productivity on these tasks. This productivity boost usually appeared in the studies as a meaningful increase in speed of execution without a significant decrease in quality. Furthermore, we observed that the willingness-to-pay for LLM-based tools is higher for people who have used the tools than those who have not, suggesting that the tools provide value above initial expectations. The report also highlights future directions for the AI and Productivity initiative, including an emphasis on approaches that capture a wider range of tasks and roles.

Studies I led that are included within this report:

• Measuring the Impact of AI on Information Worker Productivity
• Sound Like Me: Findings from a Randomized Experiment
• Randomized Controlled Trial for Microsoft Security Copilot

We conducted randomized controlled trials (RCTs) to measure the efficiency gains from using Security Copilot, including speed and quality improvements. External experimental subjects logged into a M365 Defender instance created for this experiment and performed four tasks: Incident Summarization, Script Analyzer, Incident Report, and Guided Response. We found that Security Copilot delivered large improvements on both speed and accuracy. Copilot brought improvements for both novices and security professionals.

(Also summarized in What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? at “Role-specific pain points and opportunities: Security.” Also summarized in AI and Productivity Report at “M365 Defender Security Copilot study.”)

A new version of Copilot for Microsoft 365 includes a feature to let Outlook draft messages that “Sound Like Me” (SLM) based on training from messages in a user’s Sent Items folder. We sought to evaluate whether SLM lives up to its name. We find that it does, and more. Users widely and systematically praise SLM-generated messages as being more clear, more concise, and more “couldn’t have said it better myself”. When presented with a human-written message versus a SLM rewrite, users say they’d rather receive the SLM rewrite. All these findings are statistically significant. Furthermore, when presented with human and SLM messages, users struggle to tell the difference, in one specification doing worse than random.

(Also summarized in What Can Copilot’s Earliest Users Teach Us About Generative AI at Work? at “Email effectiveness.” Also summarized in AI and Productivity Report at “Outlook Email Study.”)