Ansattprofil

Matilda Dorotic

Professor

Institutt for markedsføring

Romnummer C4Y-085
Telefon +4746410513
E-post matilda.dorotic@bi.no

Ekspertområder

Publikasjoner

Viser 5 av 12 publikasjon(er)

Seattle Journal of Technology, Environmental & Innovation Law 15(1)

54th Annual Conference of the European Marketing Academy

ACM Transactions on Intelligent Systems and Technology (TIST) Doi: https://doi.org/10.1145/3757924

1 Abstract 1.1 Objectives Biases inherent in electronic health records (EHRs), which are often used as a data source to train medical AI models, may significantly exacerbate health inequities and challenge the adoption of ethical and responsible AI in healthcare. Biases arise from multiple sources, some of which are not as documented in the literature (e.g., bias in medical devices measurement). Biases are encoded in how the data has been collected and labeled, by implicit and unconscious biases of clinicians, or by the tools used for data processing. These biases and their encoding in healthcare records can potentially undermine the reliability of such data and bias clinical judgments and medical outcomes. Moreover, when healthcare records are used to build data-driven solutions, the biases can be further exacerbated, resulting in systems that can perpetuate biases and induce healthcare disparities. This literature scoping review aims to categorize the main sources of biases inherent in EHRs. 1.2 Methods We queried PubMed and Web of Science on January 19th, 2023, for peer-reviewed sources in English, published between 2016 and 2023, using the PRISMA approach to stepwise scoping of the literature. To select the papers that empirically analyze bias in EHR, from the initial yield of 430 papers, 27 duplicates were removed, and 403 studies were screened for eligibility. 196 articles were removed after the title and abstract screening, and 96 articles were excluded after the full-text review resulting in a final selection of 116 articles. 1.3 Results Existing studies often focus on individual biases in EHR data, but a comprehensive review categorizing these biases is largely absent. To address this gap, we propose a systematic taxonomy to classify and better understand the multiplicity of biases in EHR data. Our framework identifies six primary sources: a) bias from past clinical trials ; b) data-related biases , such as missing or incomplete information; human-related biases , including c) implicit clinician bias, d) referral and admission bias, and e) diagnosis or risk disparities bias; and f) biases in devices and algorithms. This taxonomy, illustrated in Table 1, provides a valuable tool for systematically evaluating and addressing these issues. 1.4 Conclusions Machine learning and data-driven solutions can potentially transform healthcare delivery, but not without limitations. The core inputs in the systems (data and human factors) currently contain several sources of bias that are poorly documented and analyzed for remedies. The current evidence heavily focuses on data-related biases, while other sources are less often analyzed or anecdotal. However, these different sources of bias can compound each other, leading to a cumulative effect. Therefore, to understand the issues holistically we need to explore these diverse sources of bias. While racial biases in EHR have been often documented, other sources of biases have been less frequently investigated and documented (e.g. gender-related biases, sexual orientation discrimination, socially induced biases, and implicit, often unconscious, human-related cognitive biases). Moreover, some existing studies lack concrete evidence of the effects of the bias, but rather illustrate the different prevalence of disease across groups, which does not per se prove the effect of the bias. Our review shows that data-, human- and machine biases are prevalent in healthcare and can significantly affect treatment decisions and outcomes and amplify healthcare disparities. Understanding how diverse biases affect AI systems and recommendations is critical. We recommend that researchers and medical personnel develop safeguards and adopt data-driven solutions with a “bias-in-mind” approach. More empirical evidence is needed to tease out the effects of different sources of bias on health outcomes.

International Journal of Research in Marketing 41(1) s. 113-137 Doi: https://doi.org/10.1016/j.ijresmar.2023.08.010

Journal of the Academy of Marketing Science Doi: https://doi.org/10.1007/s11747-020-00759-7

Akademisk grad
År	Akademisk institusjon	Grad
2010	University of Groningen	Ph.D.
2003	Staffordshire University Business School	Master of Science
1999	University of Split, Faculty of Economics	B.A.

Arbeidserfaring
År	Arbeidsgiver	Tittel
2024 - Present	Massachusetts Institute of Technology	Adjunct Researcher
2012 - Present	BI Norwegian Business School	Assistant professor
2001 - 2012	University of Split	Research and teaching assistant
2010 - 2011	University of Groningen	Visiting researcher
2006 - 2010	University of Groningen	Ph.D. candidate

Matilda Dorotic

Ekspertområder

Publikasjoner

Defining the Social Licence of Large Language Models in Healthcare

Impact of Moral Judgments on Permissibility of High-Risk AI and Effectiveness of Privacy-Protection Solutions.

Inherent Bias in Electronic Health Records: A Scoping Review of Sources of Bias

AI on the Street: Context-dependent Responses to Artificial Intelligence

Synergistic and cannibalization effects in a partnership loyalty program

Local civil-mindedness on the internet as the basis for fundraising segmentation: Sociological, marketing determinants and the empirical analysis

With a little help from your Customers

Reward redemption effects in a loyalty program when customers choose how much and when to redeem

Advancing Research on Loyalty Programs: A Future Research Agenda

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Fire bonusfeller

Wirecutter/New York Times: "Romance, Heartbreak, and the Southwest Companion Pass", Money - Making Credit Work For You. link: https://thewirecutter.com/money/credit-cards/rewards-mania-companion-pass/

En kunst å skape lojale kunder

Impact of Moral Judgments on the Permissibility of High-Risk AI and Effectiveness of Privacy-Protection Safeguards

Epistemic humility for physicians and scientists

Impact of moral judgments on the permissibility of high-risk AI and effectiveness of privacy-protection solutions

BENEVOLENCE OR BUST: HOW INFERRED MOTIVES DRIVE PERMISSIBLITY FOR HIGH-RISK AI

Impact of Artificial Intelligence on Society: Citizens’ Responses to High-risk AI Applications

Impact of Artificial Intelligence on Society: Citizens’ Responses to High-risk AI applications

SmartFood report: Measuring impacts, scaling-up and drawing lessons learnt for Cities of the Future

Artificial Intelligence Policies

Who Will Help? The Effect of Automated Social Presence on Individuals’ Likelihood to Act Prosocially

“Engaging Citizens in Food Diversity in Cities” Talk: Impact of Growing Plants on Green Food Consumption Attitudes and Behaviors

Association for Consumer Research workshop on Consumer Privacy

Roundtable on AI & Privacy - Multiple stakeholders perspectives

AI in Public: The Effects of Technology Bias, Fears of Public Surveillance, and Moral Tradeoffs on Privacy Concerns

Seksuell utnyttelse av barn over internett: Rapport om analyse av teknologiske faktorer som påvirker produksjon og deling av materiale som seksuelt utnytter barn over internett

Child Sexual Abuse on the Internet: Report on the analysis of technological factors that affect the creation and sharing of child sexual abuse material on the Internet

Paradoxes and biases in the uptake of artificial intelligence

Artificial Intelligence Liabilities in Public: Privacy-enhancing and Transparency Effects

Anonymization in public safety AI: effects on trust, privacy concerns and surveillance fears

AI regulation and impact on humans

Citizen’s Perceptions of Anonymization and Transparency in Privacy-Enhancing Technology

Citizen’s Perceptions of Anonymization and Transparency in Privacy-Enhancing Technology

AI and Citizens' Wellbeing: Perceptions, Paradoxes and Trade-Offs For the Future of Policing

7th EDEN Conference on Data Protection in Law Enforcement: Human After All: Data Protection in Policing, Rome, 18-19 October 2021

Effects of distraction and anonymity on privacy trade-offs in facial recognition surveillance, BI Norwegian Business School, 2021

Fear and social inequality when disclosing personal information via a contact-tracing app.

Workshop on Benefits and Fears of Policing in Smart City. Focus groups data collection with seven focus groups, 47 participants. Part of the Policing in Smart City Conference, Oslo, November 2021

PRIVACY PARADOXES AND CITIZEN WELLBEING IN THE AI-ENABLED FUTURE

Keeping Loyalty Programs Fit for the Digital Age

Big Data and Social Media for Managers: How to Analyze Their True Impact on Profitability, Leaders Toolbox lecture

Unlocked Versus Locked-In Customers’ Value In Contractual Setting. EMAC 2017, by Matilda Dorotic; Evert De Haan; Socrates Mokkas

It Pays to Pay Smart: Customer benefits of introducing Cashless App Payments; by Matilda Dorotic; Koen Pauwels Special Session Paper: Customer Management: From Pay & Satisfaction To Retention & Recommendation

Customers who redeem their rewards in loyalty programs buy more

The Power of New Insights

Reward Redemption Effects in a Loyalty Program When Customers Choose When and How Much To Redeem

Symposium, University of Pennsylvania, Wharton Customer Analytics Initiative

Private Lables Strategies

BI Business Review artikler:

Big Data og sosiale medier for ledere

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