Exploring how social workers can increase their impact through futures frameworks – All content developed by Laura Burney Nissen, Ph.D., LMSW, CADCIII, Portland State University School of Social Work, Portland, Oregon, USA, Email: firstname.lastname@example.org, Twitter: @lauranissen
Category: Futures Books, Other Reading and Online Resources
I think inspiration matters. Where do our big ideas for change come from? Often they come from frustration or anxiety about things we see around us that we know should be different. Sometimes they come from history – and the lessons of those who advanced progress but didn’t yet “solve” the challenge. As scholar of social innovation (my dissertation back in the day was about macro level creativity and innovation in social work), I continue to be fascinated by how to build our inventory of innovation spaces that reflect our social work values, ethics and priorities. This is part of why a futures/foresight approach has been so valuable to me. These spaces are full of possibility. They can honor context, values, culture, history – but they inspire deeper types of questioning and more expansive ways to anticipate, explore and create with regard to what comes next, and building the requisite knowledge and power in co-creating the futures we want.
One of my favorite teaching/consulting tools is to look to “innovator” communities to scan and be nourished by good work going on in these circles. Looking through these kinds of links – refreshes my sense of how problems are framed and how solutions might be built. I’ve been building a list of some of my favorite “go to” spark-worthy sites and happy to share it here. What are some of your favorites? What inspires you? Has there ever been a more important time to reinforce our own and each other’s sense of hope and possibility for a better world?
I’m reading (a lot!) on my sabbatical. A few folks have asked me to put a list together so I did! It’s mostly general futures books (you can find other more extensive academic articles readings elsewhere on this blog). Here you go. Have fun and don’t forget to share ideas of books I may have missed. Note: I’m pretty sure I’m not going to get all of these read on my sabbatical. I have other things I’m doing…but it was a good exercise to get them all together and reprioritize which feel most important for me to read next. Isn’t a GREAT problem to have too many good books to read? I feel lucky.
As part of my own development as a futures practitioner/scholar, I have felt it very necessary to map out and cultivate a deeper set of learning aspirations to guide me and to provide the foundation for my own scholarly work in this space.
I first became aware of the term “epistemic injustice” at a social work research conference a couple of years ago – from Drs. Bonnie Duran and Roberto Orellana. Their deep wisdom and sharing of information about Indigenous ways of knowing, about assaults towards (and even more troubling – attempts to eliminate) Indigenous ways of knowing, was extremely inspiring and has stayed with me. I have continued to gather, study and reflect on what role these frameworks have to play with futures thinking and practice.
Terms like “diversity,” “equity,” and “social justice” matter and increasing tools and focus promote progress in many ways across a variety of sectors. Increasing discourse focuses on “white supremacy culture” and these frameworks are helpful in combatting inequity. That said, at this moment – I’ve found these epistemic injustice concepts the most fruitful to my own work and thinking.
In its most simple terms, a central question is: Who gets to decide what the future is? Whose dreams, aspirations, preferences, values get prioritized? Who gets to forecast what comes next – and who gets heard? Is this happening with attention and dedication to equity?
The “futures world” can (fortunately doesn’t always…) lean towards an innovation bias, a “new”-ness bias, and modern/neoliberal rhythms that can and often do, leave out many voices. While what is new is ever fascinating, it mustn’t obscure (or even more damaging – eliminate) the complex interpretations and ways of understanding what has been, what is and what comes next in the world. Epistemic injustice models deepen, complicate and strengthen social justice and equity frameworks, and as Afrofuturists, Chicano Futurists, Feminist Futurists, Queer Futurists and Indigenous Futurists (and others) are already demonstrating/practicing – diverse voices make for richer futures.
I hold these ideas with much reverence, gratitude and humility. Explore, enjoy and share. Let’s keep building a better world together.
I love to scan the literature for new information. It is a hobby, passion and fortunately – a useful pastime for a scholar! What never ceases to amaze me is the transdisplinary nature of the futures literature. It is never a dull moment in every sense!
Some months ago – I shared a project I’ve been working on. As a tool for my own scholarship, I often organize my resources in an annotated bibliography and I use these regularly as I write/study to keep myself organized. Since my goal is not only to get some papers and books out focused on my passion for futures capacity building in social work, but also to build our collective capacity to be more “foresightful” together, I am pleased to share this resource with all of you.
I’ve added a number of new articles and books that have flown across my radar screen the past couple of months. As an aid to the reader – all new entries are included in light blue text for now!
The book is a history and evolution of the power of “the big nine” tech companies (6 US-based, 3 in China), with a primary focus on the power and possibilities of artificial intelligence. It takes a deep look at all the power, opportunities, possibilities (both positive and devastating) that AI brings now and into the future.
The Big Nine defies a “simple” framework (AI is good/AI is bad). Rather it focuses on the idea that AI is almost incomprehensibly powerful and requires the responsible attention of individuals, communities and governments to assure that the highest ideals and possibilities are achieved and the greatest threats are reduced/eliminated (almost as if one might think of the way that we think about power/possibility of nuclear power – though the developmental trajectories have distinct differences).
From a social work perspective, the focus intersects with our own thinking/imagining of the “future” of social justice, human well-being and equity work. What is a future in which a few powerful (largely white, male, economically dominant and western in the US) construct underlying structures and digital machinery that decides, sorts, and controls much of the workings of modern life? How might existing inequities be replicated, multiplied – or conversely, interrupted and resolved? These are essential concerns that social work would be well-advised to factor into the way we think about the future and our work in it. How will these mechanisms (or have already begun to) (re) arrange modern life, who will continue to win and lose, and how will those trajectories play out according to the way social work thinks about ourselves and the work we aspire to do? Likely these will be a combination of ways that we are professionally familiar with (poverty, structural violence, “isms” and the like) as well as new types or variations of oppressions that we can only begin to predict and understand. My recent blog post on algorithmic transparency, bias and justice goes into some of these issues in more detail.
Our values, knowledge and skills regarding the importance and processes of engaging community voices, interrupting oppression, building more just and liberatory structures, and recognizing and addressing structural barriers to well-being could all be important skills as increasing pressure builds regarding recognizing the human rights issues associated with growth in tech that is not reflective of the well-being of all. But we will need to be intentional regarding our needed learning curve to remain relevant in these complex new spaces. I found this book to advance my own thinking/understanding regarding how vast and complex discussions of “big tech” and AI can be, and yet largely understandable using our own frames of political economy, human rights and social work ethics – just in new spaces and new ways. Social workers belong in this conversation, and this blog remains a call to action and invitation to continue dialogue about how we might best do that.
While the book as a whole is readable including three primary sections: 1) overview and evolution of tech in the modern world (formidably challenge for the non-tech reader but she does a fine job of keeping it accessible), 2) a fascinating, inspiring and sobering deep presentation of three possible “futures” concerning AI. These scenarios are crafted with the intention of fully exploring various possibilities that exist for humanity based on decisions that are made (as Ms. Webb might say) while our ability to do so is still collectively within our grasp and 3) a final section that lays out an action plan and analysis of what needs to be done to optimize all that AI has to offer, while simultaneously building a new set of global policy guardrails to protect us, in some respects, from ourselves and the worst of the risks that are increasingly apparent in the rapid evolution of these technologies. The purpose of this post is to share what I considered to be the most substantive part of the book, which is Ms. Webb’s suggestion that to succeed in the years ahead with the complexities (and risks) that AI introduces into our world, an international body comprised of tech leaders and “AI researchers, sociologists, economists, game theorists, futurists, political scientists” (p. 237) along with government leaders, and that these members reflect the “socioeconomic, gender, race, religious, political and sexual diversity” of the world (p. 237).
She calls this governing/regulatory body the Global Alliance on Intelligence Augmentation (GAIA), and their core aspirational purpose would be to collectively “facilitate and cooperate on share AI initiatives and policies” (p. 237) and to affirm and create structures to consider, operationalize and protect AI as a public good. In essence, she suggests that these tools are rapidly becoming too powerful to be left merely to the devices of private, corporate and market forces.
Here is an excerpt that clarifies what I consider to be the most important elements of this effort she proposes – which in itself is a fascinating “thought experiment” about what might be to come. I hope that we move towards this kind of global dialogue sooner rather than later – and I hope that we as social workers – can find ourselves as helpful, informative, relevant change agents, social scientists, and supporters of human well-being in an increasingly complicated world.
“GAIA should be considered a framework of rights that balances individual liberties with the greater, global good. It would be better to establish a framework that’s strong on ideals but can be more flexible in interpretation as AI matures. Member organizations would have to demonstrate they are in compliance or face being removed from GAIA. Any framework should include the following principles:
Humanity should always be at the center of AI’s development.
AI systems should be safe and secure. We should be able to independently verify their safety and security.
The Big Nine – including its investors, employees, and the governments it works within – must prioritize safety above speed. Any team working on an AI system – even those outside the Big Nine – must not cut corners in favor of speed. Safety must be demonstrated and discernable by independent observers.
If an AI system causes harm, it should be able to report out what went wrong, and there should be a governance process in place to discuss and mitigate damage.
AI should be explainable. Systems should carry something akin to a nutritional label, detailing the training data used, the processes used for learning, the real-world data being used in applications and the expected outcomes. For sensitivity or proprietary systems, trusted third parties should be able to assess and verify an AI’s transparency.
Everyone in the AI ecosystem – Big Nine employees, managers, leaders, board members; startups (entrepreneurs and accelerators); investors (venture capitalists, private equity firms, institutional investors, and individual shareholders); teachers and graduate students; and anyone else working in AI – must recognize that they are making ethical decisions all the time. They should be prepared to explain all of the decisions they’ve made during the development, testing and deployment processes.
The Human Values Atlas* should be adhered to for all AI projects. Even narrow AI applications should demonstrate that the atlas has been incorporated.
There should be a published, easy-to-find code of conduct governing all people who work on AI and its design, build and deployment. The code of conduct should also govern investors.
All people should have the right to interrogate AI systems. What an AI’s true purpose is, what data it uses, how it reaches its conclusions, and who sees results should be made fully transparent in a standardized format.
The terms of service for an AI application-or any service that uses AI – should be written in language plain enough that a third grader can comprehend it. It should be available in every language as soon as the application goes live.
PDR’s (personal data records) should be opt-in and developed using a standard format, they should be interoperable, and individual people should retain full ownership and permission rights. Should PDR’s become heritable, individual people should be able to decide the permissions and uses of their data.
PDR’s should be decentralized as much as possible, ensuring that no one party has complete control. The technical group that designs our PDRs should include legal and nonlegal experts alike: whitehat (good) hackers, civil rights leaders, government agents, independent data fiduciaries, ethicists, and other professionals working outside of the Big Nine.
To the extent possible, PDRs should be protected against enabling authoritarian regimes.
There must be a system of public accountability and an easy method for people to receive answers to questions about their data and how it is mined, refined and use throughout AI systems.
All data should be treated fairly and equally, regardless of nationality, race, religion, sexual identity, gender, political affirmations, or other uniques beliefs,” (pp. 240-242).
*The idea of a “human values atlas” is presented earlier in the book as the formidable and complex but essential task of creating a living and shared communication/document about what is most centrally valued by humans across cultures and nationalities. This atlas would guide much of the future work in the AI space – without it – we are as Ms. Webb suggests, ceding authority for these matters to potentially conflicting and hidden/opaque corporate forces. She discusses this in greater detail on pages 239-240 of the book.
Algorithms are a huge part of modern life. So much so that we sometimes forget they have arrived. Indeed they are primarily “invisible” to everyday people, working behind the scenes to sort data and make decisions that reflect the opinions of a few algorithm designers behind the scenes. Sometimes these algorithms can be life changing/life saving, for example when cancer diagnosis can be made through a combination of machine learning and algorithms that can scan hundreds of thousands of xrays to detect the tiniest irregularity that a human might miss. But other times, like racially biased facial recognition software that might inaccurately identify someone as a criminal suspect – are much more concerning. Increasingly, the ideas of “algorithmic transparency,” “algorithmic racism/bias,” and “algorithmic justice” have come into more prevalent conversation among social justice circles.
There is much learning and development going on with regard to this topic. Of all the “future facing” topics one might consider in terms of urgent need for attention in social work – in my estimation – this is one of the most important. As the rate of adoption of new technologies (most often emerging from the private sector) continues to accelerate, algorithms that don’t incorporate ethical and bias-free dimensions are a frequent point of discussion among social justice advocates. What is the pathway forward and how do we continue to increase social work practice and research attention in this area?
I would suggest that this is the most under-discussed ethical challenge of the future for the profession of social work. We need to dramatically increase the depth, range and focus of our ethical evolution to participate in and shape the future of these technologies that work for people and that prevent harm and injustice. We should concern ourselves with identifying how and where algorithms are starting to emerge and be active in our social work practice spaces (clinical and macro). Collectively – we are starting to develop a shared and critical literacy regarding these important and ubiquitous forces, and challenge a need for clear and explicit ethical guidelines/rules.
While there are pockets of enthusiasm for dialogue about these developments in social work, we have a long way to go to assert where and how we can operate most ethically – and what that looks like given the changing dynamics at play.
Here’s a reading/resource list of resources to get started – with great respect for the groundbreaking work of all who have been leaders in this space.
Dr. Desmond Patton is an Associate Professor of Social Work at Columbia University in New York City. I’ve previously listed his work on my blog but want to underscore the significant leadership he’s contributed within social work to this topic. Here’s a recent article he put together for Medium. He’s also the Principal Investigator of the Safe Lab project at Columbia which is a research initiative focused on examining the ways in which youth of color navigate violence on and offline.
Data for Black Lives is a national network of over 4,000 activists, organizers, and scientists using data science to create concrete and measurable change in the lives of Black people. For far too long, data has been weaponized against Black communities – from redlining to predictive policing, credit scoring and facial recognition. But we are charting out a new era, where data is a tool for profound social change. (From their website here!)
The Institute for the Future has developed an “Ethical OS” toolkit to provide a structure for tech experts to use to deepen their adherence to ethical principles while developing tech tools. Check it out here.
These are the books currently on my shelf on this topic:
Eubanks, V. (2018). Automating inequality: How high tech tools profile, punish and police the poor. New York: St. Martin’s Press. Review here.
Lane, J. (2019). The digital street. New York: Oxford Press. Review here.
Noble, S.U. (2018). Algorithms of oppression: How search engines reinforce racism. New York: New York University Press. Review here.
Just loved this list of qualities of good questions from Kelly (2016). Good questions are the key to being ready for new futures and ultimately, when executed well, the most human of our strengths. I’ll post a fuller review of the book (which I liked very much!) later, but until then, here’s one from the web. Consider these and add more! Thinking about this “what are the most important things for social work to do to be ready for a dynamic, unpredictable and turbulent future?” I think part of the answer…is challenging ourselves to ask better, deeper, more disruptive questions with courage and creativity…!
“A good question is like the one Albert Einstein asked himself as a small boy ‘what would you see if you were traveling on a beam of light?’ That question launched the theory of relativity (E=MC2) and the atomic age.
A good question is not concerned with a correct answer.
A good question cannot be answered immediately.
A good question challenges existing answers.
A good question is one you badly want answered once you hear it, but had no inkling you could before it was asked.
A good question creates new territory of thinking.
A good question reframes its own answers.
A good question is the seed of innovation in science, technology, art, politics and business.
A good question is a probe – a ‘what if’ scenario.
A good question skirts on the edge of what is known and not known, neither silly nor obvious.
A good question cannot be predicted.
A good question is one that generates many other questions.
A good question may be the last job a machine will ever learn to do.
A good question is what humans are for (pp. 288-289).”
Kelly, Kevin (2016). The inevitable: Understanding the 12 technological forces that will shape our future. New York: Penguin Books.