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Sarah Longwell: Why people who hate Trump stick with him

Typically, when volunteers engage in a canvassing campaign, the effort basically amounts to verbal leafleting. They make a one- to two-minute targeted pitch for a candidate or a ballot initiative, and then they leave or hang up the phone.

In a deep canvass, we want to have a real conversation. To get people to open up, we start by asking the basics: How are you doing? How are you holding up in this global pandemic? We respond not with canned answers, but with more questions: Oh, youre watching football? Who is your team? How is your family doing? Were really asking, and we really listen. Eventually, a true back-and-forth begins, one where we exchange stories about our lives and what is at stake for ourselves and for our communities in this election. Usually, by the end, what emerges is some kind of internal conflictwhy the person is frustrated, why she cant decide who to vote for, or why she is skeptical of Biden.

Recently, one of our volunteers, Angela, reached a man by phone while he was at work on a construction site (during the pandemic, weve switched from door-knocking to phone-banking). When Angela asked how he was doing, he initially said he was fine, but when Angela shared how much shes been struggling and how worried shes been about the pandemic, the conversation changed. Angela said that her husbands grandmother had died in a nursing homealong with 50 other peopleand he opened up about his wife coming down with COVID-19 and about the time that she called him at work to say she was struggling to breathe. This led to a conversation about health care and the need for good leadership. At the beginning of the call, he said he had no plans to vote but was ready to cast a ballot when he hung up, and Angela ended the call feeling a depth of connection.

Research has shown time and again that people vote from an emotional place. Its not so much that facts dont matter. Its that facts and talking points do not change minds. And arguing opinions at the start of a conversation about politics causes the interview subject to keep his defensive, partisan walls up and prevents him from connecting with the canvasser.

We don't try to directly persuade people to change their minds on a candidate or an issue. Rather, we create intimacy, in the faith that people have an ability to reexamine their politics, and their long-term worldview, if given the right context. Weve found that when people start to see the dissonance between what they believe and what they actually want, their views changemany of them come around to a more progressive perspective. For example, if a woman says she believes that immigrants are the main problem in our society, but reveals that her top personal concern is health care, then we talk about whether immigrants have anything to do with that worry. When a man says he wants to feel safe, we ask questions about what, in particular, makes him feel unsafe. If he answers COVID-19, then we talk about which candidate might be better suited to handle the pandemic.

Read the rest here:
How We Got Trump Voters to Change Their Mind - The Atlantic

William Isaac is a senior research scientist on the ethics and society team at DeepMind, an AI startup that Google acquired in 2014. He also cochairs the Fairness, Accountability, and Transparency conferencethe premier annual gathering of AI experts, social scientists, and lawyers working in this area. I asked him about the current and potential challenges facing AI developmentas well as the solutions.

A: I want to shift the question. The threats overlap, whether its predictive policing and risk assessment in the near term, or more scaled and advanced systems in the longer term. Many of these issues also have a basis in history. So potential risks and ways to approach them are not as abstract as we think.

There are three areas that I want to flag. Probably the most pressing one is this question about value alignment: how do you actually design a system that can understand and implement the various forms of preferences and values of a population? In the past few years weve seen attempts by policymakers, industry, and others to try to embed values into technical systems at scalein areas like predictive policing, risk assessments, hiring, etc. Its clear that they exhibit some form of bias that reflects society. The ideal system would balance out all the needs of many stakeholders and many people in the population. But how does society reconcile their own history with aspiration? Were still struggling with the answers, and that question is going to get exponentially more complicated. Getting that problem right is not just something for the future, but for the here and now.

The second one would be achieving demonstrable social benefit. Up to this point there are still few pieces of empirical evidence that validate that AI technologies will achieve the broad-based social benefit that we aspire to.

Lastly, I think the biggest one that anyone who works in the space is concerned about is: what are the robust mechanisms of oversight and accountability.

A: Three areas would go a long way. The first is to build a collective muscle for responsible innovation and oversight. Make sure youre thinking about where the forms of misalignment or bias or harm exist. Make sure you develop good processes for how you ensure that all groups are engaged in the process of technological design. Groups that have been historically marginalized are often not the ones that get their needs met. So how we design processes to actually do that is important.

The second one is accelerating the development of the sociotechnical tools to actually do this work. We dont have a whole lot of tools.

The last one is providing more funding and training for researchers and practitionersparticularly researchers and practitioners of colorto conduct this work. Not just in machine learning, but also in STS [science, technology, and society] and the social sciences. We want to not just have a few individuals but a community of researchers to really understand the range of potential harms that AI systems pose, and how to successfully mitigate them.

A: In 2016, I remember, the White House had just come out with a big data report, and there was a strong sense of optimism that we could use data and machine learning to solve some intractable social problems. Simultaneously, there were researchers in the academic community who had been flagging in a very abstract sense: Hey, there are some potential harms that could be done through these systems. But they largely had not interacted at all. They existed in unique silos.

Since then, weve just had a lot more research targeting this intersection between known flaws within machine-learning systems and their application to society. And once people began to see that interplay, they realized: Okay, this is not just a hypothetical risk. It is a real threat. So if you view the field in phases, phase one was very much highlighting and surfacing that these concerns are real. The second phase now is beginning to grapple with broader systemic questions.

A: I am. The past few years have given me a lot of hope. Look at facial recognition as an example. There was the great work by Joy Buolamwini, Timnit Gebru, and Deb Raji in surfacing intersectional disparities in accuracies across facial recognition systems [i.e., showing these systems were far less accurate on Black female faces than white male ones]. Theres the advocacy that happened in civil society to mount a rigorous defense of human rights against misapplication of facial recognition. And also the great work that policymakers, regulators, and community groups from the grassroots up were doing to communicate exactly what facial recognition systems were and what potential risks they posed, and to demand clarity on what the benefits to society would be. Thats a model of how we could imagine engaging with other advances in AI.

But the challenge with facial recognition is we had to adjudicate these ethical and values questions while we were publicly deploying the technology. In the future, I hope that some of these conversations happen before the potential harms emerge.

A: It could be a great equalizer. Like if you had AI teachers or tutors that could be available to students and communities where access to education and resources is very limited, thatd be very empowering. And thats a nontrivial thing to want from this technology. How do you know its empowering? How do you know its socially beneficial?

I went to graduate school in Michigan during the Flint water crisis. When the initial incidences of lead pipes emerged, the records they had for where the piping systems were located were on index cards at the bottom of an administrative building. The lack of access to technologies had put them at a significant disadvantage. It means the people who grew up in those communities, over 50% of whom are African-American, grew up in an environment where they dont get basic services and resources.

So the question is: If done appropriately, could these technologies improve their standard of living? Machine learning was able to identify and predict where the lead pipes were, so it reduced the actual repair costs for the city. But that was a huge undertaking, and it was rare. And as we know, Flint still hasnt gotten all the pipes removed, so there are political and social challenges as wellmachine learning will not solve all of them. But the hope is we develop tools that empower these communities and provide meaningful change in their lives. Thats what I think about when we talk about what were building. Thats what I want to see.

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The true dangers of AI are closer than we think - MIT Technology Review

Updated: October 25, 2020 6:51:24 pm

By Sunny Jose and Bheemeshwar Reddy A

The maiden time-use survey in India, carried out in six states in 1998-99, stated the obvious but ignored fact. We were terribly generous in confining women to primarily managing kitchens and providing unpaid care work. The proportion of men participating in, and the time spent on, unpaid domestic maintenance and care work was quite low. This revealed, inter alia, our deep allegiance to patriarchal norms, besides the burden and un-freedom women had to endure. Though this, in itself, may not be amazing, whether we have preserved it or knocked off will be interesting to see.

The latest time-use survey, carried out in all the states of India in 2019 by the National Statistical Office, comes after a gap of two decades. The 2019 time-use survey confirms the persistence of the gendered time-use patterns of the past even today. It informs that only about 26 per cent of men (six years and above) participate in either domestic maintenance or care work and a measly four per cent participate in both activities. The corresponding proportions for women are 81 per cent and 28 per cent. A closer look reveals disquieting facts. Only six per cent of men, as against 75 per cent of women, participate in food and meals management and preparation. While 13 per cent of men participate in childcare and instruction, the proportion is paltry in caring the dependent adults at home.

This huge gender gap also emerges in time spent by the participating men and women. Men spend, on average, 97 and 76 minutes, as against 299 and 134 minutes by women, in unpaid domestic and caregiving services for household members, respectively. Thus, over 80 per cent of women in India spend about five hours daily in unpaid domestic services for household members. Conversely, men spend longer time in employment related activities (459 minutes) than women (333 minutes). However, the gender gap in time spent is larger in domestic maintenance than employment related activities. Though time spent by men in domestic maintenance constitutes only 32 per cent of time spent by women, the time spent by women in employment goes up to 73 per cent of time spent by men. Interestingly, there is no perceptible gender gap in time allocation in other activities, including rest, personal care and socialisation. These broad patterns emerge in both rural and urban India.

Since education is likely to weaken mens attachment to social norms, does education help increase mens participation in unpaid domestic maintenance and care work? Do all social group exhibit the similar patterns? Surprisingly, neither the participation nor the average time spent on unpaid care and domestic activities increases substantially even if mens education goes up. Also, the same pattern appears among men from all social groups in India. These broad patterns prevail in almost all the Indian states. The notable exceptions are Assam, Arunachal Pradesh and Goa where around 50 per cent of men participate in either domestic maintenance or care work. With less than 20 per cent of mens participation, Haryana, Rajasthan and Himachal Pradesh remain at the other end. However, average time spent by men in unpaid domestic services varies substantially across the states.

Why does lower proportion of men participate in, and spend lesser time on, domestic maintenance and care work? An oft-invoked explanation is that social norms continue to condition women and men to have differential time allocation priorities and possibilities. If so, is the low participation of men and the lesser time spent by them on unpaid domestic maintenance and care work essentially due to the time poverty imposed by their long hours of employment? Or, alternatively, is it because of their adherence to social norms?

A scrutiny of time allocation patterns of older men (above 60 years) is relevant here, as time squeeze due to employment is likely to be less intense among them. Only one-third of older men as against 78 per cent of older women participate in domestic maintenance work in rural India. They spend, on average, about 112 and 245 minutes, respectively, in these activities. This pattern also appears in urban India. The lack of significant increase in mens participation and time spent on unpaid domestic and care activities despite the possible contraction in employment-induced time squeeze points to the likely influence of social norms. Whats more, the data reveals that we are schooling the younger generations (6-14 and 15-29 years) to normalise and practice such gendered patterns.

The 2019 time-use survey confirms that we continue to confine women to primarily shouldering a huge, debilitating burden of unpaid household maintenance and care work. Why do we conform to and perform the gendered time-use patterns of the past even now? Why is the educated, tech-savvy younger generation in harmony with the older generation in fostering the regressive gendered norms? It is important that we seriously take note of and deliberate on the peculiar tenacity of the archaic, gendered constructs and divisions even today and their deep but differential impacts on women and men.

Jose is RBI chair professor at Council for Social Development, Hyderabad. Reddy A is assistant professor at Department of Economics and Finance, Birla Institute of Technology & Science Pilani, Hyderabad campus. Views are personal.

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Mind the Gap - The Indian Express

(Pixabay)

The topic of bias in AI is one thats had a lot of airtime at diginomica and beyond. Bad practice involving facial recognition tech or automated candidate selection in recruitment are among the best-known examples, while this summer in the UK, pupils waiting for their vital A-Level exam results found themselves on the wrong end of an algorithm that marked them down, ruining university entry chances in many cases.

The recent (excellent) BlackTechFest conference took on some of the questions around bias in AI in a lively panel discussion that inevitably left more questions than answers in its wake, but provided food for thought. Opening the debate, Dr Djamila Amimer, CEO of AI management consultancy Mind Senses Global, began by attempting a definition of algorithmic bias:

Algorithmic bias is defined as systematic and repeatable errors by computer systems that can cause and derive unfair outcomes, such as giving privilege to one group over another...Most often data is given as the first and primary factor behind algorithmic bias....Is it really that only data that is behind algorithmic bias or do we have other factors that contribute to that algorithmic bias?

The answer to that last rhetorical question is, of course, yes, a point picked up by Shakir Mohamed, Research Scientist with DeepMind, the AI research laboratory founded in acquired by Google in 2014:

I really like that definition of systematic and reproducible error. In those two words you can actually unpack the different kinds of components of where bias is coming in. Bias is coming in whenever there is systematic bias and error coming in. So, for example, the first one will be living in a society that has a set of biases already. That bias is going to be reflected in the mindset, in the thinking, in the way that people are approaching their work unless we are very careful. Data itself has a very important role in systematic bias, but bias is coming in many areas. It is in the way we are measuring. It is in what we are even considering worthwhile measuring. Sometimes we don't have a measurement, so we fill in what's missing instead. All of these can be sources of bias.

Then there is a third source of bias, which is in the actual technical computer algorithmic system itself. We make certain choices when we are deciding what variables to use. We are compressing the model, we are making choices as to how we are building the model, using one approach versus another one. They themselves can introduce bias. You have all these different factors, combining with each other and then what you get is effectively an artificial division, a system of difference is created which empowers some and disempowers other people. We do need to be careful. I think the question of bias is a very deep one, very multi-faceted and I think it's important that we remember the multi-faceted nature that it has.

Bias in algorithms mirrors the real world, suggested Katrina Ffrench, CEO of StopWatch, a coalition of legal experts, academics, citizens and civil liberties campaigners who work together to address what they define as excess and disproportionate stop and search and to promote best practice to ensure fair, effective policing for all:

I think we need to kind of zoom back into how these algorithms come about. If theres bias in society already and the status quo is unequal and then you produce mechanisms or use tools in the same fashion, you're likely to exacerbate it.

Ffrench cited as a case in point the Gangs Matrix, a database set up by the London Metropolitan Police following civil disturbance and rioting that took place back in 2011:

What basically happened was that the police decided that they needed to identify who was at risk of criminality, specifically serious violence, so they put together this database. The main issue that we found with the database is that it was definitely discriminatory. It used a very rudimentary XML spreadsheet, into which officers would put scores to do with the harm or risk that they calculated individuals to have.

Research by Amnesty International found that 80% of the people listed on the Gangs Matrix were 12-24 year olds, 78% were black and 99% were male - and 35% of people logged had never actually committed a violent offence. The police called the database a risk management tool to prevent crime and shared its data with other official agencies. This resulted, according to FFrench, in people being denied driving licences, university places, employment and in one one instance, a child being taken into care.

The Information Commissioners Office eventually ruled that the Met Police were in breach of data protection laws, but a lot of damage was done by that time, said Ffrench:

It just felt wholly disproportionate. What the police were doing was using AI, using policing tech, to justify discriminatory policing and then most people in civil society, the young people impacted, had no understanding of it, it was incredibly difficult to challengeyou have human rights and those were breached and that's where I'm really fearful for AI and tech and the lack of transparency and the impact it can have on people's lives. Without information, [people] have no idea what they're subjected to.

So far, so depressingly familiar. But the panel then turned their attentions to what might be done to redress the balance. The temptation with examples of algorithmic bias, as in the case of the UK exams scandal, is that when they are exposed, the brakes are slammed on and a policy u-turn takes place, a practice that doesnt tackle the underlying problem. This infuriates Mind Senses Amimer:

I get really frustrated when I hear about an AI tool or an algorithm that has been shelved or has been just ditched because it was biased. I understand that if there is bias, obviously the algorithm shouldn't be in use in the first place. But where I get frustration is surely someone, somewhere could have done something about it? Is the answer always to ditch? Don't we have the power to address and to fix the bias, so we have a bias free algorithm or bias free AI?

DeepMinds Mohamed was of a similar view that u-turns are not the answer:

The way we're going to address this particular kind of problem is going to need to be at every level. It's going to be at the technical level, at the organizational level, at the regulatory level, at the societal and grassroots level. I really think the first thing we need to do is build a very broad coalition of people, coalitions between people like me who are technical designers and expert people who are on the ground who understand and see the distress [bias can cause].

He pointed to the push back against facial recognition as a case in point:

Over the last five years or so we've seen that kind of coalition from amazing women in their fields, black woman who saw this distress, wrote papers to expose the issue and then five years later building those coalitions. Every company now has decided we're not going to be involved in facial recognition. Cities and states themselves have decided to ban facial recognition. So the first solution - and maybe the hardest work - is to do that kind of broad coalition.

And its important to remember that AI and algorithms can be used to good effect, said Naomi Kellman, Senior Manager for Schools and Universities at Rare Recruitment, a specialist diversity recruitment company which aims to help employers build workforces that better reflect diversity in society:

We have built what we could also call an algorithm in the form of the Context Recruitment System. Originally top employers in certain sectors tended to look for certain type of grade profile and also certain types of work experience. That appears color blind, but it's not because we know some people have more access to good education and good opportunities. What we were able to do is build a system that looks at people's achievement in context.

So it looks at the school you went to and says, 'What does A,A,B look like in your school? Is that what everyone gets or is that the best grade anyone's got for the past few years?'. We can highlight to employers when somebody has actually outperformed in a school situation that maybe doesn't tend to produce good grades. We also collect data on people's socio-economic status - if they've been eligible for free school meals or if they grew up in the care system or if they came to the country as refugee, all things that we know have an impact on people's chances of achieving academically and we can put things in context.

This is encouraging businesses to take a wider perspective on recruiting talent, she said:

The organizations that use it now see that they interview a much broader group of people, because instead of having a very basic algorithm that says three A's or you're out, they now use all of this data to say, Actually this person has high potential, because we're looking at more data points and that means more people get hired from a wider range of backgrounds. Students are coming to see it being used in graduate recruitment and also at university level. Universities now do contextualization and they're looking to expect that from employers. So I think it's about thinking about how we can use data to broaden opportunities for people and to put things into context.

Context is key certainly. What Kellman and her organization are talking about is a very worthy goal, but a long term one that will require a lot of changed perspectives from employers in the tech space, some of whom still have lamentable track records on the diversity front. As StopWatchs Ffrench noted:

I think it's about diversity and representation. That's about tech companies doing more to recruit and to retain and to promote black professionals...until we're in those spaces, we're gonna find that these things keep replicating themselves.

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Algorithmic bias - how do we tackle the underlying problem that inhibits the full potential of AI? - Diginomica

Cover of "The New Prophet" by Kevin MacNevin Clark

MANASSAS, Va., Oct. 26, 2020 (GLOBE NEWSWIRE) -- After studying Kahlil Gibrans The Prophet, author and addiction and trauma specialist Kevin MacNevin Clark was inspired to write a modern version of the book focusing on topics related more to the internal human experience. In his debut book, The New Prophet, Mr. Clark presents deep meditative truths through the use of thoughtful metaphors and imagery around feelings such as guilt, shame, diversity, collective trauma and more that are relevant to current times as people are becoming more in touch with their emotions.

The New Prophet follows the great counselor Ishala as he returns to his hometown where he had spent many years healing wounds and providing hope to so many as he knew his final days were upon him and wanting to spend them with family. His beloved son Ezekiel sat with him through his last days, and together they shared a sacred conversation in which the son asked his father to bestow upon him his wisdom regarding the human condition. The counselor, a man who lived a life of love and service with each word he spoke and each breath he took, recounted parables and enriched his meaning through metaphor as he left his son this beautiful parting gift.

I believe there are no hopeless cases and my book is as much for those hurting as it is for the hopeful, said Mr. Clark. It will help people find emotional freedom, develop spiritually and heal hurts.

The New Prophet channels Kahlil Gibran's poetic style to provide a new perspective on commonly held attitudes and beliefsWhereas Gibran's The Prophet was a poetic celebration of what makes us human, The New Prophet is a poetic road map to remind us how we reclaim our humanity and happiness in a materialistic and individualistic world. 5-star Amazon review

Mr. Clark presents a much-needed message during these times of uncertainty in the world through his thought provoking and meditative book which provides comfort and encouragement to spiritual seekers looking to achieve a state of spiritual alignment, those in recovery, people seeking creative inspiration and more.

The New ProphetBy Kevin MacNevin ClarkISBN: 978-1-9822-5415-5 (sc); 978-1-9822-5414-8 (hc); 978-1-9822-5419-3 (e)Available through Amazon, Barnes & Noble, and Balboa Press

About the AuthorKevin MacNevin Clark found deep purpose through his work in the behavioral health field, specializing in treating addiction and trauma. He holds a degree in psychology from George Mason University and has been on his own path of awakening since 2005, getting sober and entering recovery in 2006. He founded Excelsior Addiction Services LLC in 2020 and resides in Virginia with his family, living by his guiding philosophy that there are no hopeless cases. The New Prophet is his first book and he is currently working on his next one. To learn more please follow Mr. Clark on Facebook and visit http://www.excelsioraddictionservices.com.

Balboa Press, a division of Hay House, Inc. a leading provider in publishing products that specialize in self-help and the mind, body, and spirit genres. Through an alliance with the worldwide self-publishing leader Author Solutions, LLC, authors benefit from the leadership of Hay House Publishing and the speed-to-market advantages of the self-publishing model. For more information, visit balboapress.com. To start publishing your book with Balboa Press, call 877-407-4847 today.

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Enlightening New Book 'The New Prophet' Provides Deep Meditative Truths to Awaken the Heart - GlobeNewswire