Submission Title
Responsible Gambling Algorithms: What Are We Really Measuring?
Session Title
Session 1-4-C: Lightning Talks
Presentation Type
Lightning Talk
Location
Park MGM, Las Vegas, NV
Start Date
23-5-2023 3:45 PM
End Date
23-5-2023 5:15 PM
Disciplines
Behavior and Behavior Mechanisms | Substance Abuse and Addiction
Abstract
Abstract: “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.” (Stamp, 1929). Responsible Gambling (RG) algorithms appear to have become more complex and nuanced over time, with “AI” and “machine learning” serving as buzz words to signal their legitimacy. However, as the above quote denotes, no matter how complex our models, they are only as valid as the data and outcomes upon which they are built and validated. This talk entreats the audience to return to basics and consider both the building blocks and pitfalls of RG algorithms. The talk will touch on the outcomes used to validate and evaluate these algorithms, how we distinguish involvement from risk for problems, potential algorithmic biases, and the proper use of algorithms within a larger RG system.
Implications: More and more RG interventions and regulations are tied to computerized algorithms whose purpose is to detect gamblers at-risk for gambling problems. It is crucial that we evaluate these algorithms critically to improve the algorithms themselves, as well as how we use them to prevent harm.
Keywords
algorithms, risk, gambling problems, gambling records
Funding Sources
Entain PLC (formally GVC Holdings PLC), a sports betting and gambling company, provided primary funding for this study. The Division on Addiction receives additional funding from a variety of federal, state, local, and private sources, as described at https://www.divisiononaddiction.org/funding-statement/.
Competing Interests
The Division on Addiction, through which Dr. Nelson receives her salary, receives funding from a variety of federal, state, local, and private sources, as described at https://www.divisiononaddiction.org/funding-statement/. In addition, during the past five years, Dr. Nelson has served as a paid grant reviewer for the International Center for Responsible Gaming (ICRG), GambleAware, and the National Institutes of Health (NIH). She has also received travel reimbursement and speaker honoraria from the ICRG and Responsible Gaming Association of New Mexico. She received honoraria funds for preparation of a book chapter from Universite Laval and publication royalty fees from the American Psychological Association, and received course royalty fees from the Harvard Medical School Department of Continuing Education.
Responsible Gambling Algorithms: What Are We Really Measuring?
Park MGM, Las Vegas, NV
Abstract: “The government are very keen on amassing statistics. They collect them, add them, raise them to the nth power, take the cube root and prepare wonderful diagrams. But you must never forget that every one of these figures comes in the first instance from the village watchman, who just puts down what he damn pleases.” (Stamp, 1929). Responsible Gambling (RG) algorithms appear to have become more complex and nuanced over time, with “AI” and “machine learning” serving as buzz words to signal their legitimacy. However, as the above quote denotes, no matter how complex our models, they are only as valid as the data and outcomes upon which they are built and validated. This talk entreats the audience to return to basics and consider both the building blocks and pitfalls of RG algorithms. The talk will touch on the outcomes used to validate and evaluate these algorithms, how we distinguish involvement from risk for problems, potential algorithmic biases, and the proper use of algorithms within a larger RG system.
Implications: More and more RG interventions and regulations are tied to computerized algorithms whose purpose is to detect gamblers at-risk for gambling problems. It is crucial that we evaluate these algorithms critically to improve the algorithms themselves, as well as how we use them to prevent harm.