Constance and Martin Silver Center on Data Science and Social Equity
The Constance and Martin Silver Center on Data Science and Social Equity (C+M Silver Center) supports scholarship and early development of innovations in the field of data science for social equity impact, including studies harnessing big data and using artificial intelligence. In order to leverage the growing availability and complexity of such data, the C+M Silver Center helps to develop and nurture the scientific skill set of NYU Silver faculty, doctoral students, and researchers. The Center aligns with the Grand Challenges for Social Work challenge to Harness Technology for Social Good.
The C+M Silver Center was established in June 2021 as part of a visionary $16 million gift from Dr. Constance and Martin Silver to NYU Silver to harness the emerging power of big data to identify the root causes of society’s most pressing challenges and achieve broad and transformational social impact. In addition to funding the C+M Silver Center, that gift included $5 million for the NYU McSilver Institute for Poverty Policy and Research to establish an Artificial Intelligence Hub, and funded the establishment of an Endowed Professorship in Data Science and Prevention at NYU Silver.
Our Definition of Data Science
The C+M Silver Center defines data science broadly, including the following specific domains:
o Data systems such as “big data” (large, unstructured collections of data high in volume, variety, or velocity)
o Data visualization
o Efficient processing of unstructured data
o Artificial intelligence (including an anti-racist and equitable approach to A.I., queer A.I., “emancipatory A.I.”)
o Deep learning (neural networks, etc.)
o Machine learning and other predictive modeling (including identifying and ameliorating racial bias in machine learning algorithms)
o Machine perception and sensing
o Natural language processing
o Translational data science
o Geospatial computation
o The ethics of data science
o Data science with human service populations
o Integration of data science with digital technologies
o Development of data-driven apps to address significant social problems
Principal Investigator: Dr. Victoria Stanhope (PI), Professor, NYU Silver School of Social Work.
Collaborators: Dr. Elizabeth Matthews, Dr. Sarah Shugars
Dates of award: 9/1/2021 – 8/31/2022
Amount of award: $70,500
Study description: Service disengagement is a persistent and widespread problem within the mental health system. One solution for the problem of service disengagement is to deliver person-centered care (PCC), which ensures that care is individualized and service users are active empowered partners in their treatment. This study will use an innovative artificial intelligence approach to examine Collaborative Documentation, a strategy to promote PCC in behavioral health clinics in which clinicians complete visit notes jointly with consumers during the session. The study will use natural language processing, a text mining technique that translates narrative text to structured data using an algorithm to analyze clinical visit notes. This research study will contribute to the evidence base on Collaborative Documentation and develop an algorithm to analyze PCC to inform quality improvement in behavioral health care.
Principal Investigator: Dr. Doris F. Chang, Associate Professor, NYU Silver School of Social Work.
Collaborators: Dr. Sumie Okazaki, Dr. Thu T. Nguyen, Dr. Maureen Craig
Dates of award: 9/1/2021 – 8/31/2022
Amount of award: $56,000
Study description: Anti-Asian violence and harassment have escalated during the Covid-19 pandemic, catalyzed by the racial framing of the virus, and converging with a national awakening to systematic racism following the death of George Floyd. Consistent with prior research on racism and mental health, Covid-related discrimination is associated with poorer mental health outcomes in diverse Asian American samples (Cheah et al., 2021; Stop AAPI Hate Mental Health Report, 2021). However, studies rarely consider how macro-contextual factors such as ambient racial climate (including negative views of other racial groups) and community characteristics affect racialized individuals’ psychosocial experiences, intergroup relations, and collective actions aimed at addressing racial inequality. The main aim of this study is to examine how regional variations in racial climate (as indicated by sentiment analysis of geocoded Twitter data of anti-Asian and anti-Black bias as well as solidarity and allyship across racial groups) are associated with three sets of outcomes: a) racial discrimination and mental health, b) intergroup attitudes (structural awareness, sense of belonging, political commonality/coalitional attitudes), and c) collective action and coalitional support (own-group benevolent support and political activism, Asian-Black allyship behaviors). Taking into account the diverse immigration histories and discrimination experiences of these groups, analyses will determine how regional public discourse about race affects well-being, intergroup attitudes, and collective action. The study will also examine how regional variations in residential segregation/integration and income inequality reflect community contexts for intergroup conflict, cooperation and competition (Tajfel, 1982), and may be associated with the same three sets of outcomes. Understanding the multi-level factors that shape Asian Americans’ individual and intergroup responses to racism, and subsequent civic and political engagement has important implications for community well-being and intergroup solidarity, and shaping a more inclusive, equitable, and democratic society.
C+M Silver Center Virtual Inaugural Event
Featuring Dr. Desmond Upton Patton
Founding Director of the SAFE Lab and Associate Dean for Innovation and Academic Affairs, Columbia School of Social Work
Monday, November 22, 2021