g-f(2)2441 Harnessing Beneficial Friction: Empowering Users to Catch Generative AI Errors

 

genioux Fact post by Fernando Machuca and Claude

Introduction:

In the rapidly evolving landscape of generative AI, ensuring the accuracy and reliability of AI-generated content is a critical challenge for organizations. The article "Nudge Users to Catch Generative AI Errors" from MIT Sloan Management Review explores the concept of introducing beneficial friction to enhance human oversight and intervention in AI-enabled systems. Through a field experiment conducted by MIT and Accenture, the authors demonstrate how intentionally adding cognitive speed bumps can improve the accuracy of AI-generated content without significantly compromising efficiency.

genioux GK Nugget:

"Introducing beneficial friction in AI-enabled systems can nudge users to catch generative AI errors and improve content accuracy." — Fernando Machuca and Claude, May 29, 2024

genioux Foundational Fact:

The field experiment conducted by MIT and Accenture revealed that consciously adding friction to the process of reviewing AI-generated content, in the form of error highlighting, led to increased accuracy without significantly increasing the time required to complete the task. This finding suggests that organizations can deploy generative AI applications more responsibly by assisting users in identifying parts of AI-generated content that require human scrutiny and fact-checking.

The 10 most relevant genioux Facts:

1. Humans must remain in the loop, with AI algorithms playing the role of a learning apprentice.
2. Responsible AI principles must be codified to ensure safe adoption of generative AI.
3. Accessible interfaces like ChatGPT can present errors confidently while lacking transparency about their limitations.
4. Adding friction to the process of reviewing AI-generated content can lead to increased accuracy.
5. Thoughtfulness in crafting prompts is crucial, as users tend to anchor on AI-generated output.
6. Highlighting errors draws users' attention and improves accuracy via error correction.
7. Users may overestimate their ability to identify AI-generated errors, leading to overconfidence.
8. Highlighting errors had no significant impact on participants' trust in AI tools or willingness to use them.
9. Experimenting is imperative to understand how humans interact with AI tools and their impact on accuracy, speed, and trust.
10. Humans in the loop can play an important interventional role in AI-enabled systems, with beneficial friction nudging users to exercise responsibility for content quality.

Conclusion:

As organizations increasingly adopt generative AI tools, it is crucial to recognize the importance of human oversight and intervention in ensuring the accuracy and reliability of AI-generated content. The field experiment conducted by MIT and Accenture demonstrates the effectiveness of introducing beneficial friction in the form of error highlighting to nudge users to catch generative AI errors. By embracing this approach and prioritizing responsible AI principles, organizations can harness the power of generative AI while maintaining the quality and trustworthiness of their content. As we move forward in this era of rapid AI advancement, it is essential to continue experimenting and exploring ways to optimize the balance between human intelligence and artificial intelligence, fostering a symbiotic relationship that drives innovation and progress.

REFERENCES

The g-f GK Context

Renée Richardson Gosline, Yunhao Zhang, Haiwen Li, Paul Daugherty, Arnab D. Chakraborty, Philippe Roussiere, and Patrick Connolly, Nudge Users to Catch Generative AI Errors, MIT Sloan Management Review, May 29, 2024.

ABOUT THE AUTHORS

Renée Richardson Gosline is head of the Human-First AI Group at MIT’s Initiative on the Digital Economy and a senior lecturer and research scientist at the MIT Sloan School of Management. Yunhao Zhang is a postdoctoral fellow at the Psychology of Technology Institute. Haiwen Li is a doctoral candidate at the MIT Institute for Data, Systems, and Society. Paul Daugherty is chief technology and innovation officer at Accenture. Arnab D. Chakraborty is the global responsible AI lead and a senior managing director at Accenture. Philippe Roussiere is global lead, Paris, for research innovation and AI at Accenture. Patrick Connolly is global responsible AI/generative AI research manager at Accenture Research, Dublin.

Classical Summary:

The article "Nudge Users to Catch Generative AI Errors" from MIT Sloan Management Review explores the concept of introducing beneficial friction in AI-enabled systems to enhance human oversight and improve the accuracy of AI-generated content. As organizations increasingly adopt generative AI tools like ChatGPT, they face challenges related to bias, inaccuracy, and security breaches, which can limit trust in these models. To address these concerns, the authors suggest that responsible approaches to using large language models (LLMs) are critical, emphasizing the importance of keeping humans in the loop and codifying responsible AI principles.

In a field experiment conducted by MIT and Accenture, researchers provided business research professionals with a tool designed to highlight potential errors and omissions in LLM-generated content. The study aimed to measure the extent to which adding this layer of friction reduced the likelihood of uncritical adoption of LLM content and bolstered the benefits of having humans in the loop. Participants were randomly assigned to one of three conditions with varying levels of cognitive speed bumps in the form of highlighting: full friction, medium friction, and no friction (control).

The findings revealed that introducing friction through error highlighting led to increased accuracy without significantly increasing the time required to complete the task. Participants in the no-highlight control condition missed more errors than those in the conditions with error labeling. The medium friction condition demonstrated an optimal balance between accuracy and efficiency.

The results of the experiment point to three key behavioral insights:

1. Thoughtfulness in crafting prompts is crucial, as users tend to anchor on AI-generated output.
2. Highlighting errors improves accuracy, but users may overestimate their ability to identify AI-generated errors.
3. Experimenting is imperative to understand how humans interact with AI tools and their impact on accuracy, speed, and trust.

The article concludes by emphasizing the importance of seeking ways to enhance humans' ability to improve accuracy and efficiency when working with AI-generated outputs. The study suggests that humans in the loop can play an important interventional role in AI-enabled systems and that beneficial friction can nudge users to exercise their responsibility for the quality of their organization's content.

Renée Richardson Gosline

Dr. Renée Richardson Gosline is a highly esteemed Research Scientist and Senior Lecturer at the MIT Sloan School of Management¹. She is also the head of the Human-First AI group at MIT's Initiative on The Digital Economy¹. 

Renée is an expert on the intersection between behavioral science and technology, and the implications of AI for cognitive bias in human decision-making¹. She has been named a Digital Fellow at Stanford’s Digital Economy Lab¹, an honoree on the Thinkers50 Radar List of thinkers who are “putting a dent in the universe,”¹ and one of the World’s Top 40 Professors under 40 by Poets and Quants¹. In 2024, she was recognized as a NSBE (National Society of Black Engineers) Inspire STEM honoree¹.

Renée is a leading thinker on how AI affects human judgment and the interplay of human and AI bias². She has presented her research to the White House Office of Science and Technology Policy, as a featured speaker at SXSW, and to the OECD². Her work has been published in academic journals and books and been featured in international media outlets².

Her forthcoming book, "In Praise of Friction", examines how AI affects our experiences, and the importance of auditing our decision-making processes to minimize “bad friction” and leverage “beneficial friction”². In her research experiments, she has examined a variety of topics including the persuasiveness of generative AI and human co-authorship, the impact of adding friction to generative AI to improve quality, and how cognitive style predicts preference for AI versus human input².

Renée teaches MBA and Executive Education classes, specializing in AI CX strategy, creating a culture of experimentation, and Responsible AI². Prior to academia, she was a marketing practitioner at LVMH Moet Hennessy and Leo Burnett². She received her Undergraduate, Master’s, and Doctoral degrees at Harvard University².

In 2024, Renée Richardson Gosline and Scott Stern were each awarded the 2024 Jamieson Prize for Excellence in Teaching, established to honor educational innovation and excellence¹. The Jamieson Prize for Excellence in Teaching Award is the most prestigious teaching prize offered by the School¹.

Source: Conversation with Copilot, 5/29/2024

(1) Renee Richardson Gosline | MIT Sloan. https://mitsloan.mit.edu/faculty/directory/renee-richardson-gosline.

(2) Bio | Dr. Renée Gosline. https://www.reneegosline.com/biography.

(3) Renee Richardson Gosline | MIT Sloan. https://bing.com/search?q=Ren%c3%a9e+Richardson+Gosline+biography.

(4) Renee Richardson Gosline - Stern Strategy Group. https://sternstrategy.com/speakers/renee-richardson-gosline/.

(5) Renée Richardson Gosline | Speaker & Scientist. https://www.reneegosline.com/.

Yunhao Zhang

Yunhao Zhang, also known as Jerry Zhang, is a distinguished scholar with a focus on the intersection of behavioral science and technology¹. He is currently a postdoctoral fellow at the Psychology of Technology Institute¹.

Yunhao's research interests include deep learning, data mining, and particularly sequential data modeling². His work is primarily focused on temporal sequence forecasting and analysis². He has made significant contributions to the field, with his research being published in prestigious conferences and journals¹.

One of Yunhao's notable works includes a study on fighting COVID-19 misinformation on social media, which provided experimental evidence for a scalable accuracy-nudge intervention¹. He has also conducted research on understanding and combatting misinformation across 16 countries on six continents¹.

In addition to his research, Yunhao has explored the realm of AI and human decision-making. He has investigated topics such as people's perceptions and bias towards generative AI, human experts, and human-AI collaboration in persuasive content generation¹. He has also studied how and why people abandon AI after seeing it err¹.

Before his postdoctoral fellowship, Yunhao worked towards his master's degree at the Department of Computer Science and Engineering, Shanghai Jiao Tong University². His academic journey reflects a strong foundation in both the technical and behavioral aspects of technology.

Yunhao Zhang's work continues to influence the field of technology and psychology, contributing valuable insights into the interaction between humans and AI¹.

Source: Conversation with Copilot, 5/29/2024

(1) ‪Yunhao (Jerry) Zhang‬ - ‪Google Scholar‬. https://scholar.google.com/citations?user=CjS_JNMAAAAJ.

(2) Yunhao Zhang | IEEE Xplore Author Details. https://ieeexplore.ieee.org/author/37089868129.

(3) Profile - Yunhao Zhang — CAPE. https://www.capeusa.org/profile-yunhao-zhang.

(4) Yunhao Zhang - PhD Program. https://www.iese.edu/phd-in-management/students/yuhnao-zhang/.

Paul Daugherty

Paul Daugherty is the Chief Technology and Innovation Officer (CTIO) at Accenture¹. He is a member of Accenture’s Global Management Committee and is responsible for executing Accenture’s technology strategy¹. He leads Accenture’s Innovation strategy and organization, including Accenture Labs and The Dock in Dublin, Ireland¹.

As a visionary in shaping the innovation of technology, Paul directs Accenture’s global research and development into emerging technology areas such as generative AI, quantum computing, science tech, and space technology¹. He leads a dedicated innovation group that designs and delivers transformational business and technology solutions, and also invests in, and partners with, pioneering companies to pilot and incubate new technologies¹.

Paul also leads Accenture’s annual Technology Vision report, hosts its annual Innovation Forum event, and leads Accenture Ventures, which he founded to focus on strategic equity investments to accelerate growth¹. Previously, Paul served as Accenture's Group Chief Executive – Technology, where he led all aspects of Accenture's Technology business¹.

In this role, he led the formation of Accenture Cloud First to help clients across every industry accelerate their digital transformation and realize greater value at speed and scale by rapidly becoming “cloud first” businesses¹. He oversaw the launch of the Accenture Metaverse Continuum business group, which focuses market-leading capabilities in customer experience, digital commerce, extended reality, blockchain, digital twins, artificial intelligence and computer vision to help clients design, execute and accelerate their metaverse journeys¹.

Most recently, he helped lay the groundwork for Accenture’s $3 billion investment in its Data & AI practice to help clients rapidly and responsibly advance and use AI, including Generative AI, to achieve greater growth, efficiency and resilience¹.

Paul is a passionate advocate for gender equality in the workplace and STEM-related inclusion and diversity initiatives¹. For more than six years, he has been a member of the board of directors of Girls Who Code, an organization that seeks to support and increase the number of women in computer science careers¹.

Paul serves on the board of directors of Avanade, the leading provider of Microsoft technology services¹. He also serves on the boards of the Computer History Museum and the Computer Science and Engineering program at the University of Michigan¹.

In 2023, Paul received the prestigious St. Patrick’s Day Science Medal for Industry from Science Foundation Ireland as well as a nomination to Thinkers50, a ranking of the world’s most influential management thinkers¹. He also accepted the FASPE Award for Ethical Leadership in 2019 for his work in applying ethical principles to the development and use of artificial intelligence technologies¹.

Source: Conversation with Copilot, 5/29/2024

(1) Paul Daugherty | Accenture. https://www.accenture.com/us-en/about/leadership/paul-daugherty.

(2) Meet The Enquirer's sports columnist Paul Daugherty. https://www.cincinnati.com/story/news/2020/12/31/meet-enquirers-sports-columnist-paul-daugherty/4099916001/.

(3) Paul Dougherty - Wikipedia. https://en.wikipedia.org/wiki/Paul_Dougherty.

(4) Paul Daugherty Cincinnati Wiki, Age, Daughter, Salary, Wife, Email .... https://primalinformation.com/paul-daugherty-cincinnati-wikipedia/.

(5) Staff Biographies - Exponent Philanthropy. https://www.exponentphilanthropy.org/staff-biographies/.

Arnab D. Chakraborty

Arnab D. Chakraborty is a Senior Managing Director of Accenture’s Data and AI practice and the company’s global responsible AI lead¹. With over 25 years of experience in driving large-scale, data-driven business transformations for Fortune 100 companies, Arnab has been at the forefront of innovation in artificial intelligence and data analytics¹.

His track record includes leading Accenture’s Data and AI practice in North America and expanding the company’s analytics business in Europe and North America¹. He holds more than 10 patents in machine learning solutions for real-world business challenges and has been recognized with the Franz Edelman Laureate award for advancing data-driven digital transformation¹.

Arnab’s role as the Accenture national sponsor for Upwardly Global reflects his passion for helping immigrants and refugees find their footing in the professional U.S. workforce¹. His work in this area demonstrates his commitment to diversity and inclusion, which are key values at Accenture¹.

In addition to his leadership role at Accenture, Arnab is also the Chief Responsible AI Officer at Accenture PLC². In this role, he oversees the ethical use of AI within the company and ensures that Accenture's AI systems are designed and used responsibly².

Arnab's work continues to influence the field of technology and AI, contributing valuable insights into the responsible use of AI in business¹².

Source: Conversation with Copilot, 5/29/2024

(1) Arnab Chakraborty - Upwardly Global. https://www.upwardlyglobal.org/leadership/arnab-chakraborty/.

(2) Arnab Chakraborty, Accenture PLC: Profile and Biography. https://www.bloomberg.com/profile/person/23978442.

(3) Arnab Chakraborty - Upwardly Global. https://bing.com/search?q=Arnab+D.+Chakraborty+biography.

(4) Dr Arnab Chakraborty | UNSW Research. https://research.unsw.edu.au/people/dr-arnab-chakraborty.

(5) Untitled | Department of Urban & Regional Planning. https://urban.illinois.edu/people/profiles/arnab-chakraborty-aicp/.

Philippe Roussiere

Philippe Roussiere is the Global Lead for Research Innovation and AI at Accenture, based in Paris¹. With over 25 years of experience in various research leadership roles, he has been instrumental in guiding Accenture to new levels of innovation and impact¹.

Roussiere's work involves scaling innovative methods like machine learning, natural language processing, economic modeling, data visualization, and hybrid/experiential research platforms¹. His current focus is on Generative AI and data productization to increase productivity and creativity¹.

Throughout his career, Roussiere has worked on both client-focused and thought leadership projects, consistently pushing the boundaries of what is possible in the realm of AI and innovation¹. His contributions have helped Accenture regularly reach new levels of innovation and impact¹.

Source: Conversation with Copilot, 5/29/2024

(1) Philippe Roussiere - iResearch Services. https://events.iresearchservices.com/event-speakers/philippe-roussiere/.

(2) Philippe Roussiere | Biography - MutualArt. https://www.mutualart.com/Artist/Philippe-Roussiere/2637195E2132940E/Biography.

(3) Philippe Roussière - Harvard Business Review France. https://www.hbrfrance.fr/experts/philippe-roussiere.

Patrick Connolly

Patrick Connolly is the Global Responsible AI and Generative AI Research Manager at Accenture Research, based in Dublin, Ireland⁵⁶. He plays a pivotal role in shaping Accenture's approach to responsible AI and generative AI⁶.

Patrick's work involves exploring the ethical implications of building conversational AI tools⁵. His research focuses on how to ensure that conversational AI is trusted and how to address the unique ethical risks associated with advanced conversational AI⁵. He has been involved in developing an approach that considers the intricacies of technology development and human rights in tandem⁵.

In addition to his research, Patrick has contributed to the development of tools designed to highlight potential errors and omissions in large language model (LLM) content⁶. His work has shown that consciously adding some friction to the process of reviewing LLM-generated content can lead to increased accuracy without significantly increasing the time required to complete the task⁶.

Before joining Accenture, Patrick earned a Master of Science in Marketing and Communications from Florida State University in 2000-2001⁷. His academic background and professional experience make him a valuable asset in the field of AI research and development.

Patrick Connolly's work continues to influence the field of technology and AI, contributing valuable insights into the responsible use of AI in business⁵⁶.

Source: Conversation with Copilot, 5/29/2024

(1) Building Trust Into Conversational AI | Accenture. https://www.accenture.com/in-en/insights/cloud/conversational-ai.

(2) Nudge Users to Catch Generative AI Errors - Tribune Content Agency. https://tribunecontentagency.com/article/nudge-users-to-catch-generative-ai-errors/.

(3) Patrick Connolly email address & phone number | Accenture Global .... https://rocketreach.co/patrick-connolly-email_17818220.

(4) Patrick Connolly - Wikipedia. https://en.wikipedia.org/wiki/Patrick_Connolly.

(5) Patrick Connolly | Philosophy | Johns Hopkins University. https://philosophy.jhu.edu/directory/patrick-connolly/.

(6) John Patrick Connolly - Wikipedia. https://en.wikipedia.org/wiki/John_Patrick_Connolly.

(7) About Me — Patrick Connolly | Photojournalist. https://www.pconphoto.com/aboutme.

(8) Patrick Connolly Profile: Contact Information & Network - PitchBook. https://pitchbook.com/profiles/person/188438-77P.

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