Filter Results: (315) Arrow Down

TechnologyRemove Technology →

New research on technology from Harvard Business School faculty on issues including technological innovation, technology networks, and technology adoption.
Page 1 of 315 Results →

Why Confronting Racism in AI 'Creates a Better Future for All of Us'

by Barbara DeLollis

Rather than build on biased data and technology from the past, artificial intelligence has an opportunity to do better, says Business in Global Society Fellow Broderick Turner. He highlights three myths that prevent business leaders from breaking down racial inequality.

How Should Artificial Intelligence Be Regulated—if at All?

by James Heskett

Some AI pioneers say the technology could be a risk to humanity, and some governments have taken steps to rein it in. But who should set the rules and what details must they consider? asks James Heskett. Open for comment; 0 Comments.

How SHEIN and Temu Conquered Fast Fashion—and Forged a New Business Model

by John Deighton

The platforms SHEIN and Temu match consumer demand and factory output, bringing Chinese production to the rest of the world. The companies have remade fast fashion, but their pioneering approach has the potential to go far beyond retail, says John Deighton.

Is Amazon a Retailer, a Tech Firm, or a Media Company? How AI Can Help Investors Decide

by Danielle Kost

More companies are bringing seemingly unrelated businesses together in new ways, challenging traditional stock categories. MarcAntonio Awada and Suraj Srinivasan discuss how applying machine learning to regulatory data could reveal new opportunities for investors.

Can AI and Machine Learning Help Park Rangers Prevent Poaching?

Re: Brian L. Trelstad

Globally there are too few park rangers to prevent the illegal trade of wildlife across borders, or poaching. In response, Spatial Monitoring and Reporting Tool (SMART) was created by a coalition of conservation organizations to take historical data and create geospatial mapping tools that enable more efficient deployment of rangers. SMART had demonstrated significant improvements in patrol coverage, with some observed reductions in poaching. Then a new predictive analytic tool, the Protection Assistant for Wildlife Security (PAWS), was created to use artificial intelligence (AI) and machine learning (ML) to try to predict where poachers would be likely to strike. Jonathan Palmer, Executive Director of Conservation Technology for the Wildlife Conservation Society, already had a good data analytics tool to help park rangers manage their patrols. Would adding an AI- and ML-based tool improve outcomes or introduce new problems? Harvard Business School senior lecturer Brian Trelstad discusses the importance of focusing on the use case when determining the value of adding a complex technology solution in his case, “SMART: AI and Machine Learning for Wildlife Conservation.”

ChatGPT: Did Big Tech Set Up the World for an AI Bias Disaster?

by Scott Van Voorhis

Google tried to silence AI bias warnings from ethicist Timnit Gebru. Will a world enamored with OpenAI's ChatGPT be able to confront them? Tsedal Neeley reflects on Gebru's experience in a case study, and offers advice on managing the ethical risks of AI.

COVID-19 Lessons: Social Media Can Nudge More People to Get Vaccinated

by Scott Van Voorhis

Social networks have been criticized for spreading COVID-19 misinformation, but the platforms have also helped public health agencies spread the word on vaccines, says research by Michael Luca and colleagues. What does this mean for the next pandemic?

Why We Still Need Twitter: How Social Media Holds Companies Accountable

by Kasandra Brabaw

Remember the viral video of the United passenger being removed from a plane? An analysis of Twitter activity and corporate misconduct by Jonas Heese and Joseph Pacelli reveals the power of social media to uncover questionable situations at companies.

Addressing Racial Discrimination on Airbnb

Re: Michael Luca

For years, Airbnb gave hosts extensive discretion to accept or reject a guest after seeing little more than a name and a picture, believing that eliminating anonymity was the best way for the company to build trust. However, the apartment rental platform failed to track or account for the possibility that this could facilitate discrimination. After research published by Professor Michael Luca and others provided evidence that Black hosts received less in rent than hosts of other races and showed signs of discrimination against guests with African American sounding names, the company had to decide what to do. In the case, “Racial Discrimination on Airbnb,” Luca discusses his research and explores the implication for Airbnb and other platform companies. Should they change the design of the platform to reduce discrimination? And what’s the best way to measure the success of any changes?

Why Technology Alone Can't Solve AI's Bias Problem

by Michael Blanding

Engineers designed "fair-ranking algorithms" to prevent artificial intelligence from marginalizing certain groups. While these tools help, research by Himabindu Lakkaraju finds that they can't completely override the most stubborn source of bias: people.

Metaverse Seoul: How One City Used Citizen Input to Pilot a Government-Run Metaverse

Re: Mitchell B. Weiss

In May 2022, the Seoul Metropolitan Government in Seoul, Korea launched the pilot of Metaverse Seoul, a virtual version of Seoul’s mayor’s office. As they worked towards building a broad, immersive, online government platform, they hoped to gain insights from citizens about everything from popular local tourist sites that could be experienced virtually to government services that could be delivered in the metaverse. But to do that, the team had to figure out how to solicit ideas from citizens and then determine which ideas to put to use. Professor Mitchell Weiss discusses their approach, as well as questions relating to his research on public entrepreneurship and what he calls “possibility government,” in his case, “Metaverse Seoul.”

Is There a Method to Musk’s Madness on Twitter?

by Christina Pazzanese, Harvard Gazette

Elon Musk's brash management style has upended the social media platform, but was bold action necessary to address serious problems? Andy Wu discusses the tech entrepreneur's takeover of Twitter.

Buy Now, Pay Later: How Retail's Hot Feature Hurts Low-Income Shoppers

by Rachel Layne

More consumers may opt to "buy now, pay later" this holiday season, but what happens if they can't make that last payment? Research by Marco Di Maggio and Emily Williams highlights the risks of these financing services, especially for lower-income shoppers.

Why TikTok Is Beating YouTube for Eyeball Time (It’s Not Just the Dance Videos)

by John Deighton and Leora Kornfeld

Quirky amateur video clips might draw people to TikTok, but its algorithm keeps them watching. John Deighton and Leora Kornfeld explore the factors that helped propel TikTok ahead of established social platforms, and where it might go next.

When Design Enables Discrimination: Learning from Anti-Asian Bias on Airbnb

by Pamela Reynolds

Airbnb bookings dropped 12 percent more for hosts with Asian names than other hosts during the early months of the COVID-19 pandemic, says research by Michael Luca. Could better design deter bias, particularly during times of crisis?

Can Amazon Remake Health Care?

by Christina Pazzanese, Harvard Gazette

Amazon has disrupted everything from grocery shopping to cloud computing, but can it transform health care with its One Medical acquisition? Amitabh Chandra discusses company's track record in health care and the challenges it might face.

A Lesson from Google: Can AI Bias be Monitored Internally?

Re: Tsedal Neeley

Dr. Timnit Gebru was the co-lead of Google’s Ethical AI research team—until she raised concerns about bias in the company’s large language models and was forced out in 2020. Her departure sent shockwaves through the AI and tech community and raised fundamental questions about how companies safeguard against bias in their own AI. Should in-house ethics research continue to be led by researchers who best understand the technology, or must ethics and bias be monitored by more objective researchers who aren’t employed by companies? Professor Tsedal Neeley discusses how companies can approach the problem of AI bias in her case, “Timnit Gebru: 'SILENCED No More' on AI Bias and The Harms of Large Language Models.”

Burgers with Bugs? What Happens When Restaurants Ignore Online Reviews

by Kara Baskin

Negative Yelp reviews hold more sway with consumers than restaurateurs might think. A machine learning study by Chiara Farronato reveals how online platforms amplify the customer voice, and why business owners should listen.

Free Isn’t Always Better: How Slack Holds Its Own Against Microsoft Teams

by Lane Lambert

What will it take to win the collaboration app wars: massive scale or a loyal following? A case study by David Yoffie digs into the intense competition between Microsoft Teams and Salesforce's Slack.

Delivering a Personalized Shopping Experience with AI

Re: Jill J. Avery

THE YES, a shopping app for fashion brands, uses a sophisticated algorithm to create and deliver a personalized store for every shopper, based on her style preferences, size, and budget. After launching the app in 2020, the founders had to decide whether to continue developing the algorithm to deliver on the company’s customer value proposition or to focus their resources on new customer acquisition, with the idea that more users on the app would improve the algorithm's performance. Senior Lecturer Jill Avery and The YES co-founder and CEO Julie Bornstein discuss this make-or-break dilemma in the case, The YES: Reimagining the Future of e-Commerce with Artificial Intelligence (AI). This episode was recorded live at Harvard Business School on March 30, 2022 as part of our Case Method 100 celebration.