Periodic Reporting for period 1 - FPH (Fair predictions in health)
Periodo di rendicontazione: 2021-09-01 al 2023-08-31
Problem Statement:
There are significant diagnostic disparities in medical AI, with algorithms potentially misdiagnosing diseases differently across genders and ethnicities due to biased data and uniform thresholds.
Societal Impact:
As AI becomes prevalent in healthcare, addressing potential biases in diagnostic tools is vital. These biases can significantly affect patient well-being, making a deep understanding of fairness in AI essential.
Research Objectives:
The project aims to examine philosophical concepts of justice and fairness, the intersection of probability theory and ethics, and real-world case studies to identify and address biases in AI healthcare diagnostics.
Project Conclusion:
My research, rooted in the "fair equality of chances" principle, reveals complex algorithmic biases in healthcare that reflect broader societal inequalities. It underscores the need for a nuanced understanding of fairness in healthcare AI to avoid perpetuating these disparities.
There are significant diagnostic disparities in medical AI, with algorithms potentially misdiagnosing diseases differently across genders and ethnicities due to biased data and uniform thresholds.
Societal Impact:
As AI becomes prevalent in healthcare, addressing potential biases in diagnostic tools is vital. These biases can significantly affect patient well-being, making a deep understanding of fairness in AI essential.
Research Objectives:
The project aims to examine philosophical concepts of justice and fairness, the intersection of probability theory and ethics, and real-world case studies to identify and address biases in AI healthcare diagnostics.
Project Conclusion:
My research, rooted in the "fair equality of chances" principle, reveals complex algorithmic biases in healthcare that reflect broader societal inequalities. It underscores the need for a nuanced understanding of fairness in healthcare AI to avoid perpetuating these disparities.
**Overview of Academic Contributions and Publications:**
My recent academic journey includes a range of collaborative and individual projects. One of our key publications, an article exploring the relevance of causality in discrimination, was co-authored with colleagues from Politecnico di Milano (Polimi) and published in the respected journal Res Publica. Additionally, I have developed a conceptual map that delves into the nuances of fairness in healthcare decisions based on predictive models, which is currently under submission for publication.
In a related vein, my exploration into the legitimacy of using predictions to justify inequalities has been a prolific area of research. This exploration has culminated in three articles: two of which have been published in the FAccT '22 conference proceedings, and another is eagerly awaited in the journal Economics and Philosophy (online access in advance of publication is available). Further extending this research theme, I have co-authored a paper with a US colleague, now under review.
A notable highlight is the development of a process model for a value-based design methodology. This model, designed to integrate ethical considerations into design processes, is currently accessible as a pre-print and is under revision for future publication.
**Dissemination and Educational Activities:**
My dissemination efforts have been multifaceted. I successfully led a reading group, fostering deep discussions and insights among its members. Additionally, I conducted a seminar at the Computational Cancer Biology lab of the Istituto Europeo di Oncologia, which was tailored towards early career researchers and emphasized the importance of fairness in machine learning training.
In the educational domain, I delivered guest lectures in a computer ethics course, providing valuable insights into the ethical dimensions of technology. The course "Teaching Philosophy with Statistics," initially intended for high-school teachers, underwent some adaptations to better align with its educational goals. These efforts have been instrumental in spreading knowledge and stimulating thought-provoking discussions.
On a global scale, our research findings have been showcased at several prestigious conferences, including the ACM FACCT Conference in Seoul and the European Workshop on Algorithmic Fairness, where I served as one of the co-chairs.
**Outreach and Public Engagement:**
In terms of public engagement, I collaborated with Algorithmwatch in a public debate and contributed to a comprehensive blog article. This was an opportunity to reach a broader audience and engage in meaningful discourse on technology and ethics. Further, I developed an educational module titled "Inclusive Machine Learning" for the Responsible Innovators of Tomorrow program, which is now available on the EDx Platform.
In addition to these efforts, I have created and disseminated open-source teaching materials focusing on algorithmic discrimination and equity. These materials are designed to be accessible and informative, catering to educators and the wider community. They are available for download at http://effediesse.mate.polimi.it](http://effediesse.mate.polimi.it(si apre in una nuova finestra).
My work has also been featured in the media, particularly through my participation in two podcasts. I was interviewed on "Ethical Machines" by Reid Blackman, targeting industry leaders, and on "The ReadME Project," appealing to open-source developers. These interviews provided a platform to discuss the manuscript on value-based design methodology, further extending the reach and impact of my research.
My recent academic journey includes a range of collaborative and individual projects. One of our key publications, an article exploring the relevance of causality in discrimination, was co-authored with colleagues from Politecnico di Milano (Polimi) and published in the respected journal Res Publica. Additionally, I have developed a conceptual map that delves into the nuances of fairness in healthcare decisions based on predictive models, which is currently under submission for publication.
In a related vein, my exploration into the legitimacy of using predictions to justify inequalities has been a prolific area of research. This exploration has culminated in three articles: two of which have been published in the FAccT '22 conference proceedings, and another is eagerly awaited in the journal Economics and Philosophy (online access in advance of publication is available). Further extending this research theme, I have co-authored a paper with a US colleague, now under review.
A notable highlight is the development of a process model for a value-based design methodology. This model, designed to integrate ethical considerations into design processes, is currently accessible as a pre-print and is under revision for future publication.
**Dissemination and Educational Activities:**
My dissemination efforts have been multifaceted. I successfully led a reading group, fostering deep discussions and insights among its members. Additionally, I conducted a seminar at the Computational Cancer Biology lab of the Istituto Europeo di Oncologia, which was tailored towards early career researchers and emphasized the importance of fairness in machine learning training.
In the educational domain, I delivered guest lectures in a computer ethics course, providing valuable insights into the ethical dimensions of technology. The course "Teaching Philosophy with Statistics," initially intended for high-school teachers, underwent some adaptations to better align with its educational goals. These efforts have been instrumental in spreading knowledge and stimulating thought-provoking discussions.
On a global scale, our research findings have been showcased at several prestigious conferences, including the ACM FACCT Conference in Seoul and the European Workshop on Algorithmic Fairness, where I served as one of the co-chairs.
**Outreach and Public Engagement:**
In terms of public engagement, I collaborated with Algorithmwatch in a public debate and contributed to a comprehensive blog article. This was an opportunity to reach a broader audience and engage in meaningful discourse on technology and ethics. Further, I developed an educational module titled "Inclusive Machine Learning" for the Responsible Innovators of Tomorrow program, which is now available on the EDx Platform.
In addition to these efforts, I have created and disseminated open-source teaching materials focusing on algorithmic discrimination and equity. These materials are designed to be accessible and informative, catering to educators and the wider community. They are available for download at http://effediesse.mate.polimi.it](http://effediesse.mate.polimi.it(si apre in una nuova finestra).
My work has also been featured in the media, particularly through my participation in two podcasts. I was interviewed on "Ethical Machines" by Reid Blackman, targeting industry leaders, and on "The ReadME Project," appealing to open-source developers. These interviews provided a platform to discuss the manuscript on value-based design methodology, further extending the reach and impact of my research.
In my deep dive into healthcare and the challenges posed by artificial intelligence (AI) and machine learning (ML), I have expanded my knowledge horizon by exploring the dimensions of algorithmic bias. Through an interdisciplinary lens, I have critically examined both individual- and group-level causality, unraveling the moral and ethical nuances of fairness, risk, and deservingness. My critique of existing fairness frameworks emphasizes their limitations and lays the groundwork for a more comprehensive approach to tackle the multifaceted nature of algorithmic bias. Drawing inspiration from the Fair Equality of Chances principle, as introduced by myself and colleagues, I have pioneered a unique perspective, showcasing how these biases, subtle yet profound, operate beyond overt discriminatory actions. My goal is clear: advocate for a holistic understanding of fairness in healthcare algorithms, emphasizing the recognition of biases and the moral quandaries they pose.
As I shared my specialized expertise on fairness in machine learning with POLIMI, it became a symbiotic relationship, enriching both the institution and strengthening my pedagogical skills. Drawing from my foundational background in ethics and aligning with my supervisor's expertise in epistemology and probability, we fostered a multidisciplinary collaboration. This blossomed further through joint authorships with various researchers from diverse fields like mathematical statistics, computer science, and philosophy of science. Notably, collaborations with local researchers, among other esteemed peers from both European and US departments, have been instrumental in this integrative scholarly journey.
As I imparted my advanced competencies on fairness in machine learning to POLIMI, it was an exchange of knowledge, strengthening my teaching experience. My background in ethics, complemented by my supervisor's strength in epistemology and probability, has paved the way for collaborative growth through the joint authorship of scientific articles.
While I faced resistance in traditional academic settings, my commitment has never waned. Venturing beyond academia, I am now harnessing my interdisciplinary knowledge to influence real-world scenarios, offering consultancy on fairness and AI to diverse stakeholders.
The importance of AI ethics transcends academia. By offering AI ethics consultations to NGOs like Algorithmwatch and corporations, I'm directly influencing real-world applications. Additionally, recognizing the broader societal implications, I have made efforts to assist in governmental regulation activities by applying to serve as an expert for the ECAT office of the European Union. This endeavor represents my commitment to ensuring that advancements in AI are ethically sound and beneficial for all.
As I shared my specialized expertise on fairness in machine learning with POLIMI, it became a symbiotic relationship, enriching both the institution and strengthening my pedagogical skills. Drawing from my foundational background in ethics and aligning with my supervisor's expertise in epistemology and probability, we fostered a multidisciplinary collaboration. This blossomed further through joint authorships with various researchers from diverse fields like mathematical statistics, computer science, and philosophy of science. Notably, collaborations with local researchers, among other esteemed peers from both European and US departments, have been instrumental in this integrative scholarly journey.
As I imparted my advanced competencies on fairness in machine learning to POLIMI, it was an exchange of knowledge, strengthening my teaching experience. My background in ethics, complemented by my supervisor's strength in epistemology and probability, has paved the way for collaborative growth through the joint authorship of scientific articles.
While I faced resistance in traditional academic settings, my commitment has never waned. Venturing beyond academia, I am now harnessing my interdisciplinary knowledge to influence real-world scenarios, offering consultancy on fairness and AI to diverse stakeholders.
The importance of AI ethics transcends academia. By offering AI ethics consultations to NGOs like Algorithmwatch and corporations, I'm directly influencing real-world applications. Additionally, recognizing the broader societal implications, I have made efforts to assist in governmental regulation activities by applying to serve as an expert for the ECAT office of the European Union. This endeavor represents my commitment to ensuring that advancements in AI are ethically sound and beneficial for all.