Name: Brainstorm Design 2024: Design In Focus - Designing For Equity, Access & Justice
Uploaded: 2024-12-05
Duration: 15 min 18 s
Description: Renée Cummings, Assistant Professor of the Practice in Data Science, University of Virginia

Renée Cummings, Assistant Professor of the Practice in Data Science, University of Virginia

Brainstorm Design 2024: Design In Focus - Designing For Equity, Access & Justice

Conferences

Thank you so very much. Let me say it's an absolute honor and a pleasure to be with you. I'm a criminologist and criminal psychologist. I'm also a therapeutic jurisprudence specialist and I'm also a terrorism specialist and that is the work that I bring to data science. When we think about equity, when we think about access and when we think about justice, we have got to think about designing with these concepts, using these concepts as frameworks to audit this technology. We know that A I is a revolutionary technology. We know that A I is a radical technology, but we have to understand that there is no A I without data, data is the most critical component. It is the most critical component, the lifeblood of this technology. So when we think about data, we have got to think about the frameworks which we use to really ensure sure that we are developing and designing and deploying a technology that is equitable, a technology that is just and a technology that creates access. We know that data creates very, very unique challenges when it comes to bias, when it comes to discrimination, when it comes to systemic challenges. We also know that data if left in a space and a state of really not being managed could really create some extraordinary challenges. So when we think about data, I like to say, we have got to bring an ethical approach to the ways in which we're doing data. We have got to think about how do we use equity and access and justice so that we do not undermine data accuracy or we do not undermine data integrity, data fidelity or even our data validity. These are very, very critical questions. So when we think about access, we have got to think about visibility, we have got to think about voice. We have got to think about vulnerable groups and the ways in which we can ensure we are building a just future. We are co creating a just future for everyone to be a part of it. When we think about justice, we have got to think about questions around due process. One of the things that we have seen with data and with algorithms is that an algorithm has the ability to undermine in real time questions around agency questions around access questions around self actualization. An algorithm has the ability to make a decision about you without you even knowing a decision was made. So for us to really bring an approach that is equitable approach that creates access, an approach that is justice oriented. We have always got to think about bringing a very sophisticated level of due diligence to the ways in which we are designing because it is about trust, it is about responsibility and it is about ensuring that this technology benefits humanity. We also have to think about questions around due process, questions around uh just the ways in which we need to bring a requisite level of duty of care, the ways in which we need to bring the duty to warn and the ways in which we need to bring ethical vigilance to what we're doing. So let me just start with questions around duty of care. We know that A I has extraordinary promise. We know that A I is not only pervasive, but we know that A I has the potential to just do extraordinary things. But we know this technology also brings extraordinary risk where there are risks. It means that there are responsibilities and those responsibilities require us to protect the rights of the individuals who are engaging with this technology, the rights of entire societies. We have got to ensure that the technology is not only responsible and trustworthy, but we're understanding questions around resources, data and A I require an extraordinary amount of resources. Uh The impact on the environment is something that many of us do not think about. We speak about building a sustainable and resilient future using this technology, but we've got to think about the ways in which the environment is also being impacted by this technology. So we have a duty of care, a responsibility as a data scientist, as a critical data scientist, as individuals who are designing technology, we have a responsibility to ensure that we bring a framework to audit the ways in which we are building models, the ways in which we are building algorithms. We have got to ensure that we are not only bringing this very sophisticated and heightened level of diligence. We have got to ensure that we are understanding that responsibility, our duty of care. We have got to ensure we have got to ensure that everything that we do with this technology understands this question of due process, equity and fairness coming together to create situations of justice. When I speak about data, I always speak about this data pipeline. And some of the things that I like to look at would be questions around the provenance of data. It's so important when we're designing to ask ourselves, where did this data come from? I always speak of the anatomy of data, the genealogy of data, the pathology of data, sometimes we're using data that is being duplicated in very weird ways. Data that's actually missing pieces, critical pieces to the stories that need to be told. So when we are using data, when we are bringing this heightened level of due diligence, when we are paying attention to this duty of care, it means as designers using data, as designers building algorithms, as designers of a I the space in which I work, we have always got to ensure that everything that we do, we bring this heightened level of ethical vigilance. So when we think about access, we have got to ask ourselves when we are designing, when we're developing and when we are building, who are we building for? Are we using this technology to create access or are we using this technology in ways that are going to deny access ways that are going to deny access to resources that are going to deny opportunities? We've got to think about that. When we think about equity, we have got to understand that data equity is business intelligence, data equity is economic intelligence. Data equity is also about opportunity, but it's also about empowering. When we think about justice, we know if we are designing, developing procuring a technology that is unjust a technology that is unfair, then we are not designing for the greater good. We've got to think about those questions. We've also got to think about the ways in which we engage communities that is so important to the work that we're doing stakeholder engagement, whether or not the products, the policies, the processes we are designing? Are they being community inspired? Are they being community led? What's the level of stakeholder engagement? How involved are communities in the process? It's very, very critical the ways in which we engage communities to design because we are realizing that many of our data sets, many of the algorithms that we're deploying are creating very, very critical risks. So aspect of what we do is that ability to mitigate those risks to, to manage those risks, to monitor those risks. So if we use equity and access and justice as frameworks to audit, if when we're designing and developing and deploying and procuring, we are thinking about those vulnerability audits and those impact assessments and those risk management approaches and those crisis management approaches and the ways in which we are building our business continuity plans. If we are thinking about them within that framework of equity access and justice, it means we are designing for good. It means that we are designing and engaging and educating in the kinds of structures or systems that we are creating. We also have got to think about for me the legacy. And I always say what I have learned as a critical data scientist, what I have learned as someone involved in A I and data governance is the question of legacy that is attached to data. When we think about data, when we think about algorithms, we have got to think about the legacies that we are deploying with an algorithm. As I said earlier, an algorithm has the ability to make a decision about you not only in real time, but without you even knowing a decision has been made. So when you think of your own identity, when you think about the ways in which you want to engage with this technology. You have got to think about the legacy when you are designing, I design with data, when you are designing, what is the legacy you want this algorithm to leave? And it's a very, very critical question because an algorithm has the ability to create access or deny access, to create resources and deny resources and to define legacy and undermine legacies. And it's something that we have got to think about. We've also got to think about the imagination and how the imagination intersects with equity and access and justice. We've got to think about a radical imagination. And I think from the few days that I have spent in Macau, I have really experienced the possibilities and the potential of a radical imagination because what I have seen among the fusion of cultures is an enduring spirit and this grand idea of possibilities and imagining the impossible and then designing it. So as we think about designing, using a framework of equity, access and justice, we have got to ask ourselves, what is our relationship with our communities? What is our relationship with society? What is our responsibility as data scientists, as technologists, as designers, as policymakers leaders? What is our responsibility as we co create a future together? How are we going to ensure that we are building equity and access and justice into our designs? How are we going to ensure that we are bringing that ethical vigilance that is required. Because what have realized with data, what we have realized with A I is that A I is not a technical tool A I is a socio technical tool because of the extraordinary impact it has on society and continues to have the other challenge with A I and gen A I is that we are yet to truly understand the extent of this technology and what this technology can do, we know that it can do great things. And we are seeing that in healthcare, we are seeing that in communication. We are seeing just about every discipline and every industry reimagining business model using A I and using gen A I. So we understand the extraordinary potential, but we've also got to understand the extraordinary risks. We've got to understand the responsibilities. We've got to understand our rights. We've also got to understand that data is now a question of justice algorithms are now a question of justice. When we are designing, we've got to think about human rights and civil rights and data rights and algorithmic rights. These are big questions. What data has allowed us to do is answer some of the world's greatest questions around the environment, around health care, around criminal justice. But what data has also done is create new ethical challenges. So if we want to think about technology in a responsible way, if we want to think about technology in a way that benefits humanity, if we want to think about technology for good A I for good and, and doing good data science, we have got to think about a framework that is built on equity access and justice. Because if we do not, then what we are going to create would be systems that not only undermine our own progress but systems that could create situations that undermine the ways in which we move into the future together. So as you build, as you design, as you develop, as you deploy, as you procure, I ask you when you are auditing, when you are doing those vulnerability audits and those impact assessments, you have got to bring that question of equity access and justice because if we do not do it, then we are at risk of creating a future that may not even welcome us into that space. So as we dream and as, as we imagine, let us think of the impossible and let us use that framework to really deploy what is possible for all of us. And with that, I thank you.