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AT THE end of Black History Month, it is important to reflect on the crucial juncture for race relations that we find ourselves in. Across the world, racism and the far right are on the rise. Yet we have also seen the largest mobilisation of anti-racist protest for decades in the form of the inspiring Black Lives Matter movement.

It has never been more important for us to learn from the history of racial oppression and to end the injustices that exist to this day. Yet the government has chosen Black History Month to wage war against an accurate teaching of institutional racism in our schools.

During a debate on Black History Month, Kemi Badenoch MP, the Exchequer Secretary to the Treasury who is also the Women and Equalities Minister, strongly criticised the Black Lives Matter movement and declared that schools teaching critical race theory will be breaking the law. She prohibited teachers from telling children about the fact that white privilege exists.

This means that our government is in auspicious company, as a month previously President Donald Trump declared that critical race theory (CRT) is like a cancer, and signed executive orders banning its use in federal agency training schemes.

We should be very alarmed that our government is directly copying culture war strategies from Donald Trumps racist playbook. Yet even more than that, we should be worried by their refusal to recognise the reality of institutional racism.

During the Black Lives Matter movement, weve rightly seen renewed calls for our schools to teach the true brutal history of the British empire and the legacy of imperialism, colonialism and racism which continue today to have generational impact.

Present day global inequalities remain permanently shaped by the horrors of extractive colonialism and racialised subordination. It is unacceptable that instances of appalling murder and violence at the hands of the British state have been erased from present-day memory of empire.

It is barely known, for instance, that one fifth of the billionaires in Britain owe their wealth to the transportation of our Black ancestors. If we are to end the scourge of institutional racism and the destructive legacy of colonialism, it is vital that young people are taught the true history of race relations.

Despite what our government believes, it is simply not the case that the existence of institutional racism is up for debate. For instance, it is beyond dispute that Covid-19 has had a disproportionate impact on Black, Asian and minority ethnic communities. The latest ONS data on ethnic contrasts in Covid-19 deaths showed that in England and Wales, males of black African ethnic background had the highest rate of death, which was 2.7 times higher than males of white ethnic background. Women of a black Caribbean ethnic background also had the highest rate, which is 2.0 times higher than females of white ethnic background.

These inequalities are grounded in class inequalities and reflect the severe racial disparities in our economy. The Resolution Foundation think tank estimate that Black, Indian, Pakistani and Bangladeshi employees experience an annual pay penalty of 3.2 billion. The grim intersection of racial and class discrimination has had a deadly consequence during this pandemic.

In May, I asked the Prime Minister how he intended to protect African, Asian and minority ethnic communities from the virus.

Five months later, his government has refused to take any actions that would specifically protect our communities. If it is unwilling to even recognise the connection between economic and physical wellbeing, it is clear this government is not serious about combatting health inequalities.

Many have tried to dismiss the imbalance in deaths as being explained by cultural or even genetic differences. Yet discrimination is deeply ingrained in our social, political, and economic structures.

The scourge of institutional racism results in unequal access to quality education, healthy food, liveable wages, and affordable housing which are the foundations of health and wellbeing.

According to the Office for National Statistics, key workers are more likely than average to be from Black, Asian or minority ethnic communities, be women, be born outside the UK, and be paid less than the average UK income. An Institute for Public Policy Research (IPPR) study in September 2020 showed that of all the people from minority ethnic groups who were employed or self-employed at the start of the crisis, 13 per cent had lost their job by June compared to 5 per cent of the overall population.

The IPPR thinktank, who published research with the Runnymede Trust, found that almost 60,000 more deaths involving coronavirus could have occurred in England and Wales if white people faced the same risk as black communities.

It is two years since the Conservative government launched its consultation on ethnicity pay reporting which sought to enable government and employers to move forward in a consistent and transparent way. The consultation closed in January 2019 but still the government have not reported back on it or confirmed a date for mandatory ethnicity pay reporting to start.

The governments decision to wage war on critical race theory reveals their contempt for African, Asian and minority ethnic communities. We on the left cannot allow their divide and rule culture wars to win. We must keep pushing for economic and public health support for our communities, and keep fighting against the divisive tactics of this administration.

Claudia Webbe MP is the Member of Parliament for Leicester East. You can follow her at facebook.com/claudiaforLE/ and twitter.com/ClaudiaWebbe

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The government's divide and rule culture wars must be opposed - Morning Star Online

NewsOpinionColumnistsIn America, they call them counters; the old British-English word tellers seems to have vanished from Americas election vocabulary, at least in the knife-edge states that still remain undeclared as I write, following this weeks US election.

Friday, 6th November 2020, 7:00 am

Whatever the differences of language, though, the intense coverage of the election process over the last few days has served to remind us of how little we see of everyday America, on our screens, and particularly of America outside New York, Washington and Los Angeles. We see crises and killings and protests, of course; and we see the glamorous high-profile journalists who rush to cover those dramatic events for the main television channels.

Its relatively rare, though, for us to spend hours watching ordinary America, in Georgia, or Arizona, or Pennsylvania, just going about its business; in this case, huge rooms full of volunteers and state staffers counting votes, verifying them, inviting adjudicators from both main parties to rule on any uncertain ballots, and trying in the face of a historically high voter turnout, and a pandemic that has decimated the labour force while generating an unprecedented surge in postal votes to deliver a fair and accurate result.

In the face of this calm and methodical effort to get a vitally important job done, the disruptive comments emanating from Donald Trumps White House seem not only tasteless and insulting, but also somehow unreal; as if they come from some different planet where Trump is not the leader of the Republican Party, and his party does not have designated observers present, at every count currently under way.

This sense of detachment from reality, though, has been a distinguishing feature of Trumps politics since the start of his first presidential campaign. Like right-wing populists across the world from Nigel Farage in the UK to Viktor Orban in Hungary and Jair Bolsonaro in Brazil he relies on his ability to conjure up, for his followers, a largely fictional world of dire threats and simple, aggressive solutions.

The classic example is Trumps characterisation of most migrants crossing the Mexican border as criminals and rapists, and his declared policy of "building a wall to stop them. Such tropes and visions, though, are the common stuff of reactionary politics in our time; and its therefore perhaps not surprising that this week, America has seen the election to Congress of at least one, and possibly several, elected representatives who are fully paid-up subscribers to the Qanon conspiracy theory, a complete bizarre belief-system entirely elaborated and spread via social media, since 2017 that now commands the support of millions worldwide; to the extent that the family and friends of those affected in the US, Europe and beyond are beginning to seek advice on how to get through to loved ones who have become obsessed by their Qanon beliefs.

Whoever emerges as the winner of this weeks historic US election battle, in other words, the country will remain deeply divided between those who have embraced Trumps world-view and the conspiracy theories to which he has often given online support and those who regard these beliefs as delusional and dangerous.

It is good, of course, to hear Joe Biden affirm that he will, if elected, try to unite the country; but its also wise to note that under 21st-century conditions, those who seek unity and reconciliation will often be dealing not just with the usual differences of political opinion about ends and means, but with differences of world-view so categorical that they seem, at first glance, to make conversation, argument or persuasion all but impossible.

Whether the subject in hand is the Trump presidency, the Brexit debate, or the idea of Scottish independence, What world are you living in? has become one of the most commonly used phrases in all internet debate; and in these times, it is often something more than a rhetorical question.

Yet there are still, I think, a few reasons to be cheerful about the long task of restoring a more productive and realistic dialogue about our possible futures; not least because persuasion does not always have to be verbal.

If people who believe in a society founded on freedom, democracy, equality, and mutual respect among citizens make a point of going out into their communities and trying to embody those beliefs; if they set agreed goals for that community, and work to deliver those with the same practical, methodical dedication shown by those vote counters this week; if they are kind, accepting of diversity, willing to listen and to share small pleasures well then, they can show by example how a good society should look, and draw people into that network of shared values and conversation.

National politicians can try, of course, to win the culture wars that have disfigured our recent politics by meeting rhetoric with rhetoric, and toxic speeches with more conciliatory ones.

In the end, though, it seems likely that a renewed sense of unity, in any nation wounded by division, can only be rebuilt from the grassroots up, through the kind of patient, hard work that draws people away from their screens, and forms real human bonds.

On Wednesday night, those thousands of patient counters across the United States succeeded in making the strident voices from the White House look both marginal and irrelevant. And with that kind of dedication, ordinary people in communities across the world can finally deliver the same verdict on those who attempt the politics of hysteria and hate; the judgment that, while frightening and sometimes alluring, those ways of thought are finally, in the practical business of life, just empty of substance, and of no real use at all.

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Can democracy cope amid the rise of dangerous conspiracy theories and toxic culture wars? Joyce McMillan - The Scotsman

Ding-dong, the jerk is gone. Finally, we have come to the end of Donald Trumps season of extreme misrule. Voters have rejected what can only be described as the crassest, vainest, stupidest, most dysfunctional leadership this country has ever suffered.

Congratulations to Joe Biden for doing what Hillary Clinton couldnt, and for somehow managing to do it without forcefulness, without bounce, without zest, without direction and without a real cause, even.

It is a time for celebrating. Let us praise God for victory, however meagre and under-whelming. But let us also show some humility in our triumph. Before we swing into a national sing-along of the Hallelujah Chorus, I urge you to think for a moment about how we got here and where we must go next.

We know that 2020 has been a year for reckoning with the racist past, for the smashing of icons and the tearing-down of former heroes. Also for confronting the historical delusions that gave us this lousy present.

In the spirit of this modern iconoclasm, let me offer my own suggestion for the reckoning that must come next, hopefully even before Biden chooses his cabinet and packs his bags for Pennsylvania Avenue: Democrats must confront their own past and acknowledge how their own decisions over the years helped make Trumpism possible.

I know: this was a negation election, and what got nixed was Maga madness. The Democrats are the ones who won. Still, it is Joe Biden who must plan our course forward and so it is Biden who must examine our situation coldly and figure out the answer to the burning question of today: how can a recurrence of Trumpism be prevented?

Bidens instinct, naturally, will be to govern as he always legislated: as a man of the center who works with Republicans to craft small-bore, business-friendly measures. After all, Bidens name is virtually synonymous with Washington consensus. His years in the US Senate overlap almost precisely with his partys famous turn to the third way right, and Biden personally played a leading role in many of the signature initiatives of the era: Nafta-style trade agreements, lucrative favors for banks, tough-on-crime measures, proposed cuts to social security, even.

What Biden must understand now, however, is that it was precisely this turn, this rightward shift in the 1980s and 90s, that set the stage for Trumpism.

Let us recall for a moment what that turn looked like. No longer were Democrats going to be the party of working people, they told us in those days. They were new Democrats now, preaching competence rather than ideology and reaching out to new constituencies: the enlightened suburbanites; the wired workers; the learning class; the winners in our new post-industrial society.

For years this turn was regarded as a great success. Bill Clinton brought us market-friendly reforms to banking rules, trade relations and the welfare system. He and his successor Barack Obama negotiated grand bargains and graceful triangulations; means-tested subsidies and targeted tax credits; tough-minded crime measures and social programs so complex that sometimes not even their designers could explain them to us.

Almost all of the celebrated policy achievements of the centrist era lie in ruins

In the place of the Democratic partys old household god the middle class these new liberals enshrined the meritocracy, meaning not only the brilliant economists who designed their policies, but also the financiers and technologists that the new liberalism tried to serve, together with the highly educated professionals who were now its most prized constituents. In 2016 Hillary Clinton lost the former manufacturing regions of the country but was able to boast later on that she won the places that represent two-thirds of Americas gross domestic product the places that are optimistic, diverse, dynamic, moving forward.

However, there are consequences when the left party in a two-party system chooses to understand itself in this way. As we have learned from the Democrats experiment, such a party will show little understanding for the grievances of blue-collar workers, people who by definition have not climbed the ladder of meritocracy. And just think of all the shocking data that has flickered across our attention-screens in the last dozen years how our economys winnings are hogged by the 1%; how ordinary people can no longer afford new cars; how young people are taking on huge debt burdens right out of college; and a thousand other points of awful. All of these have been direct or indirect products of the political experiment I am describing.

Biden cant take us back to the happy assumptions of the centrist era even if he wants to, because so many of its celebrated policy achievements lie in ruins. Not even Paul Krugman enthuses about Nafta-style trade agreements any longer. Bill Clintons welfare reform initiative was in fact a capitulation to racist tropes and brought about an explosion in extreme poverty. The great prison crackdown of 1994 was another step in cementing the New Jim Crow. And the biggest shortcoming of Obamas Affordable Care Act leaving peoples health insurance tied to their employer has become painfully obvious in this era of mass unemployment and mass infection.

But the biggest consequence of the Democrats shabby experiment is one we have yet to reckon with: it has coincided with a period of ever more conservative governance. It turns out that when the party of the left abandons its populist traditions for high-minded white-collar rectitude, the road is cleared for a particularly poisonous species of rightwing demagoguery. It is no coincidence that, as Democrats pursued their professional-class third way, Republicans became ever bolder in their preposterous claim to be a workers party representing the aspirations of ordinary people.

When Democrats abandoned their majoritarian tradition, in other words, Republicans hastened to stake their own claim to it. For the last 30 years it has been the right, not the left, that rails against elites and that champions our down-home values in the face of the celebrities who mock them. During the 2008 financial crisis conservatives actually launched a hard-times protest movement from the floor of the Chicago board of trade; in the 2016 campaign they described their foul-mouthed champion, Trump, as a blue-collar billionaire, kin to and protector of the lowly the lowly and the white, that is.

Donald Trumps prodigious bungling of the Covid pandemic has got him kicked out of office and has paused the nations long march to the right. Again, let us give thanks. But let us also remember that the Republicans have not been permanently defeated. Their preening leader has gone down, but his toxic brand of workerism will soon be back, enlisting the disinherited and the lowly in the cause of the mighty. So will our fatuous culture wars, with their endless doses of intoxicating self-righteousness, shot into the veins of the nation by social media or Fox News.

I have been narrating our countrys toboggan ride to hell for much of my adult life, and I can attest that Bidens triumph by itself is not enough to bring it to a stop. It will never stop until a Democratic president faces up to his partys mistakes and brings to a halt the ignoble experiment of the last four decades.

Should Joe Biden do that, he might be able to see that he has before him a moment of great Democratic possibility. This country has grown sick of plutocracy. We dont enjoy sluicing everything we earn into the bank accounts of a few dozen billionaires. We want a healthcare system that works and an economy in which ordinary people prosper, even people who didnt go to a fancy college. Should Biden open his eyes and overcome his past, he may discover that he has it in his power to rebuild our sense of social solidarity, to make the middle-class promise real again, and to beat back the right. All at the same time.

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Ding-dong, the jerk is gone. But read this before you sing the Hallelujah Chorus - The Guardian

Many of us may not even understand exactly where or what the Cloud is.

Yet, much of the data and programs that control our lives live on this Cloud of distant computer servers with the directions to run our devices coming over the Internet.

As the prevalence of artificial intelligence (AI)-driven devices grows, researchers would like to bring some of that decision-making back to our own devices. WSU researchers have developed a novel framework to more efficiently use AI algorithms on mobile platforms and other portable devices. They presented their most recent work at the 2020 Design Automation Conference and the 2020 International Conference on Computer Aided Design.

The goal is to push intelligence to mobile platforms that are resource-constrained in terms of power, computation, and memory, said Jana Doppa, George and Joan Berry Associate Professor in the School of Electrical Engineering and Computer Science. This has a huge number of applications ranging from mobile health, augmented and virtual reality, self-driving cars, digital agriculture, and image and video processing mobile applications.

Voice-recognition software, mobile health, robotics, and Internet-of-Things devices all use artificial intelligence to keep society moving at an ever-faster and automated pace. Self-driving cars powered by AI algorithms remain somewhere on the not-too-distant horizon.

The decisions for these increasingly sophisticated devices are all made in the Cloud, but as demands increase, the Cloud can become increasingly problematic, Doppa said. For instance, it isnt fast enough. Having a device in a self-driving car decide to turn right while looking both ways requires that information go from the car to the Cloud and then back to the car.

The time required to make decisions might not meet real-time requirements, said Partha Pande, Boeing Centennial Chair professor in School of EECS, who collaborated in this research.

Many rural or under-developed areas also dont have easy access to the infrastructure needed for the requirements of AI related communications and transferring information back and forth through the Cloud can also raise privacy concerns.

At the same time, however, requiring sophisticated computer algorithms to run on portable devices is also problematic. Computational resources havent been good enough, a phones computing memory is small, and a lot of decision-making will quickly drain the battery power.

We need to run the algorithms in a resource-constrained environment, Pande said.

Doppas group came up with a framework that is able to run complex neural network-based algorithms locally using less power and computation.

The researchers took an approach that prioritizes problem solving. As in human decision-making in which problems vary in their complexity and require more or less brain power, the researchers developed a framework in which their algorithms spend a lot of energy on only the complex parts of problems while using less resources for the easy ones.

By doing this, we are improving performance and saving a lot of energy, Doppa said.

So, for instance, in a digital agriculture application, their more efficient software and hardware could be embedded on a UAV, which could efficiently make decisions about crop spraying with less computational and energy requirements.

The researchers have applied their algorithms to virtual/augmented reality as well as image editing applications. The researchers are the first to adapt state-of-the-art AI approaches for structured outputs to a mobile platform. These include Graph Convolution Networks (GCNs), which are used to produce three-dimensional object shapes from images in augmented and virtual reality, and Generative Adversarial Networks (GANs) technology, which is used to generate synthetic images. In the case of the GAN technology, the solution the researchers developed was able to achieve a more than 50% savings in energy for a loss of about 10% in accuracy.

Since mobile platforms are constrained by resources, there is a great need for low-overhead solutions for these emerging GCNs and GANs to perform energy-constrained inference, said Nitthilan Kanappan Jayakodi, a graduate student in the School of Electrical Engineering and Computer Science who was lead author on the research and was selected as a Richard Newton Young Fellow from the ACM Special Interest Group on Design Automation for his outstanding research contributions. To the best of our knowledge, this is the first work on studying methods to deploy emerging GCNs and GANs to predict complex structured outputs on mobile platforms.

The work was funded by the National Science Foundation and the U.S. Army Research Office.

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Artificial intelligence on the edge | WSU Insider | Washington State University - WSU News

The increasing attention being paid to artificial intelligence raises important questions about its integration with social sciences and humanity, according to David De Cremer, founder and director of the Centre on AI Technology for Humankind at the National University of Singapore Business School. He is the author of the recent book, Leadership by Algorithm: Who Leads and Who Follows in the AI Era?

While AI today is good at repetitive tasks and can replace many managerial functions, it could over time acquire the general intelligence that humans have, he said in a recent interview with AIfor Business (AIB),a new initiative at Analytics at Wharton. Headed by Wharton operations, information and decisions professor Kartik Hosanagar, AIB is a research initiative that focuses on helping students expand their knowledge and application of machine learning and understand the business and societal implications of AI.

According to De Cremer, AI will never have a soul and it cannot replace human leadership qualities that let people be creative and have different perspectives. Leadership is required to guide the development and applications of AI in ways that best serve the needs of humans. The job of the future may well be [that of] a philosopher who understands technology, what it means to our human identity, and what it means for the kind of society we would like to see, he noted.

An edited transcript of the interview appears below.

AI for Business: A lot is being written about artificial intelligence. What inspired you to write Leadership by Algorithm? What gap among existing books about AI were you trying to fill?

David De Cremer: AI has been around for quite some time. The term was coined in 1956 and inspired a first wave of research until the mid-1970s. But since the beginning of the 21st century more direct applications became clear and changed our attitude towards the real potential of AI. This shift was especially fueled by events where AI started to engage with world champions in chess and the Chinese game Go. Most of the attention went, and still goes to, the technology itself: that the technology acts in ways that seem to be intelligent, which is also a simple definition of artificial intelligence.

It seems intelligent in ways that humans are intelligent. I am not a computer scientist; my background is in behavioral economics. But I did notice that the integration between social sciences, humanity, and artificial intelligence was not getting as much attention as it should. Artificial intelligence is meant to create value for society that is populated by humans; the end users always must be humans. That means AI must act, think, read, and produce outcomes in a social context.

AI is particularly good at repetitive, routine tasks and thinking systematically and consistently. This already implies that the tasks and the jobs that are most likely to be taken over by AI are the hard skills, and not so much the soft skills. In a way, this observation corresponds with what is called Moravecs paradox: What is easy for humans is difficult for AI, and what is difficult for humans seems rather easy for AI.

An important conclusion is then also that in the future developments of humans, training our soft skills will become even more important and not less as many may assume. I wanted to explain that because there are many signs today especially so since COVID-19 that we need and are required to adapt more to the new technologies. As such, that puts the use and influence of AI in our society in a dominant position. As we are becoming more aware, we are moving into a society where people are being told by algorithms what their taste is, and, without questioning it too much, most people comply easily. Given these circumstances, it does not seem to be a wild fantasy anymore that AI may be able to take a leadership position, which is why I wanted to write the book.

We are moving into a society where people are being told by algorithms what their taste is, and, without questioning it too much, most people comply easily.

AIB: Is it possible to develop AI in a way that makes technology more efficient without undermining humanity? Why does this risk exist? Can it be mitigated?

De Cremer: I believe it is possible. This relates to the topic of the book as well. [It is important] that we have the right kind of leadership. The book is not only about whether AI will replace leaders; I also point out that humans have certain unique qualities that technology will never have. It is difficult to put a soul into a machine. If we could do that, we would also understand the secrets of life. I am not too optimistic that it will [become reality] in the next few decades, but we have an enormous responsibility. We are developing AI or a machine that can do things we would never have imagined years ago.

At the same time, because of our unique qualities of having and taking perspective, proactive thinking, and being able to take things into abstraction, it is up to us how we are going to use it. If you look at the leadership today, I do not see much consensus in the world. We are not paying enough attention to training our leaders our business leaders, our political leaders, and our societal leaders. We need good leadership education. Training starts with our children. [It is about] how we train them to appreciate creativity, the ability to work together with others, take perspectives from each other, and learn a certain kind of responsibility that makes our society. So yes, we can use machines for good if we are clear about what our human identity is and the value we want to create for a humane society.

AIB: Algorithms are becoming an important part of how work is managed. What are the implications?

De Cremer: An algorithm is a model that makes data intelligent, meaning it helps us to recognize the trends that are happening in the world around us, and that are captured by means of our data collections. When analyzed well, data can tell us how to deal with our environment in a better and more efficient manner. This is what Im trying to do in the business school, by seeing how we can make our business leaders more tech savvy in understanding how, where, and why to use algorithms, automation, to have more efficient decision-making.

Many business leaders have problems making business cases for why they should use AI. They are struggling to make sense of what AI can bring to their companies. Today most of them are influenced by surveys showing that as a business you have to engage in AI adoption because everyone else is doing it. But how it can benefit your own unique company is often less well understood.

Every company has data that is unique to it. You must work with that in terms of [shaping] your strategy, and in terms of the value that your company can and wishes to create. For this to be achieved, you also have to understand the values that define your company and that make it different your competitors. We are not doing a good job training our business leaders to think like this. Rather than making them think that they should become coders themselves, they should focus on becoming a bit more tech savvy so they can pursue their business strategy in line with their values in an environment where technology is part of the business process.

This implies that our business leaders do understand what an algorithm exactly does, but also what its limits are, what the potential is, and especially so where in the decision-making chain of the company AI can be used to promote productivity and efficiency. To achieve this, we need leaders who are tech savvy enough to optimize their extensive knowledge on business processes to maximize efficiency for the company and for society. It is there that I see a weakness for many business leaders today.

Without a doubt, AI will become the new co-worker. It will be important for us to decide where in the loop of the business process do you automate, where is it possible to take humans out of the loop, and where do you definitely keep humans in the loop to make sure that automation and the use of AI doesnt lead to a work culture where people feel that they are being supervised by a machine, or being treated like robots. We must be sensitive to these questions. Leaders build cultures, and in doing this they communicate and represent the values and norms the company uses to decide how work needs to be done to create business value.

AIB: Are algorithms replacing the human mind as machines replaced the body? Or are algorithms and machines amplifying the capabilities of the mind and body? Should humans worry that AI will render the mental abilities of humans obsolete or simply change them?

De Cremer: That is one of the big philosophical questions. We can refer to Descartes here, [who discovered the] body and mind [problem]. With the Industrial Revolution, we can say that the body was replaced by machine. Some people do believe that with artificial intelligence the mind will now be replaced. So, body and mind are basically taken over by machines.

We can use machines for good if we are clear about what our human identity is and the value we want to create for a humane society.

As I outlined in my book, there is more sophistication to that. We also know that the body and mind are connected. What connects them is the soul. And that soul is not a machine. The machine at this moment has no real grasp of what it means to understand its environment or how meaning can be inferred from it. Even more important in light of the idea of humanity and AI, a machine does not think about humans, or what it means to be a human. It does not care about humans. If you die today, AI does not worry about that.

So, AI does not have a connection to reality in terms of understanding semantics and deeply felt emotions. AI has no soul. That is essential for body and mind to function. We say that one plus one is three if you want to make a great team. But in this case if we say AI or machines replace the body and then replace the mind, we still have one plus one is two, but we do not have three, we dont have the magic. Because of that, I do not believe AI is replacing our mind.

Secondly, the simple definition that I postulated earlier is that artificial intelligence represents behaviors, or decisions that are being made by a machine that seem intelligent. That definition is based on the idea that machine intelligence is able to imitate the intelligent behavior that humans show. But, that machines seem able to act in ways like humans does not mean that we are talking about the same kind of intelligence and existence.

When we look at machine learning, it is modeled after neural networks. But we also know, for example, that neuroscience still knows little, maybe not even 10%, of how the brain works. So, we cannot say that we know everything and put that in a machine and argue that it replicated the human mind completely.

The simplest example I always use is that a computer works in ones and zeroes, but people do not work in ones and zeroes. When we talk about ethics with humans, things are mostly never black or white, but rather gray. As humans we are able to make sense of that gray area, because we have developed an intuition, a moral compass in the way we grew up and were educated. As a result, we can make sense of ambiguity. Computers at the moment cannot do that. Interestingly, efforts are being made today to see whether we can train machines like we educate children. If that succeeds, then machines will come closer to dealing with ambiguity as we do.

AIB: What implications do these questions have for leadership? What role can leaders play in encouraging the design of better technology that is used in wiser rather than smarter ways?

That machines seem able to act in ways like humans does not mean that we are talking about the same kind of intelligence and existence.

De Cremer: I make a distinction between managers and leaders. When we talk about running an organization, you need both management and leadership. Management provides the foundation for companies to work in a stable and orderly manner. We have procedures so we can make things a little bit more predictable. Since the early 20th century, as companies grew in size, you had to manage companies and [avoid] chaos. Management is thus the opposite of chaos. It is about structuring and [bringing] order to chaos by employing metrics to assess goals and KPIs are achieved in more or less predictable ways. In a way, management as we know it, is a status-quo maintaining system.

Leadership, however, is not focused on the status quo but rather deals with change and the responsibility to give direction to deal with the chaos that comes along with change. That is why it is important for leadership to be able to adapt, to be agile, because once things change, as a leader you are looked upon to [provide solutions]. That is where our abilities to be creative, to think in proactive ways, understand what value people want to see and to adapt to ensure that this kind of value is achieved when change sets in.

AI will be extremely applicable to management because management is consistent, it tries to focus on the status quo, and because of its repetitiveness it is in essence a pretty predictable activity and this is basically also how an algorithm works. AI is already doing this kind of work by predicting the behavior of employees, whether they will leave the company, or whether they are still motivated to do their job. Many managerial decisions are where I see algorithms can play a big role. It starts as AI being an advisor, providing information, but then slowly moving into management jobs. I call this management by algorithm MBA. Theoretically and from a practical point of view, this will happen, because AI as we know it today in organizations is good at working with stationary data sets. It, however, has a problem dealing with complexities. This is where AI, as we know it today, falls short on the leadership front.

Computer scientists working in robotics and with self-driving cars say the biggest challenge for robots is interacting with people, physical contact, and coordinating their movements with the execution of tasks. Basically, it is more difficult for robots to work within the context of teams than sending a robot to Mars. The reason for this is that the more complex the environment, the more likely it is that robots will make mistakes. As we are less tolerant to having robots inflict harm on humans, it thus becomes a dangerous activity to have autonomous robotsand vehicles interacting with humans.

Leadership is about dealing with change. It is about making decisions that you know are valuable to humans. You need to understand what it means to be a human, that you can have human concerns, taking into account that you can be compassionate, and you can be humane. At the same time, you need to be able to imagine and be proactive, because your strategy in a changing situation may need to be adjusted to create the same value. You need to be able to make abstraction of this, and AI is not able to do this.

AIB: I am glad you brought up the question of compassion. Do you believe that algorithm-based leadership is capable of empathy, compassion, curiosity, or creativity?

[Artificial intelligence] has a problem dealing with complexities. This is where AI, as we know it today, falls short on the leadership front.

De Cremer: Startups and scientists are working on what we call affective AI. Can AI detect and feel emotions? Conceptually it is easy to understand. So, yes, AI will be able to detect emotions, as long as we have enough training data available. Of course, emotions are complex also to humans so, really understanding what emotions signify to the human experience, thats something AI will not be able to do (at least in decades to come). As I said before, AI does not understand what it means to be human, so, taking the emotional intelligence perspective of what makes us human is clearly a limit for machines. That is also why we call it artificial intelligence. It is important to point out that we can also say that humans have an AI; I call that authentic intelligence.

At this moment AI does not have authentic intelligence. People believe that AI systems cannot have authentic emotions and an authentic sense of morality. It is impossible because they do not have the empathic and existential qualities people are equipped with. Also, I am not too sure that algorithms achieve authentic intelligence easily given the fact that they do not have a soul. So, if we cannot infuse them with a common sense that corresponds to the common sense of humans, which can make sense of gray zones and ambiguity, I dont think they can develop a real sense of empathy, which is authentic and genuine.

What they can learn and that is because of the imitation principle is what we call surface-level emotions. They will be able to respond, they will scan your face, they will listen to the tone of your voice, and they will be able to identify categories of emotions and respond to it in ways that humans usually respond to. That is a surface-level understanding of the emotions that humans express. And I do believe that this ability will help machines to be efficient in most interactions with humans.

Why will it work? Because as humans we are very attuned to the ability of our interaction partners to respond to our emotions. So almost immediately and unconsciously, when someone pays attention to us, we reciprocate. Recognizing surface-level emotions would already do the trick. The deeper-level emotions correspond with what I call authentic intelligence, which is genuine, and an understanding of those type of emotions is what is needed to develop friendships and long-term connections. AI as we know it today is not even close to such an ability.

With respect to creativity, it is a similar story. Creativity means bringing forward a new idea, something that is new and meaningful to people. It solves a problem that is useful, and it makes sense to people. AI can play a role there, especially in identifying something new. Algorithms are much faster than humans in connecting information because they can scan, analyze, and observe trends in data so much faster than we do. So, in the first stage of creativity, yes, AI can bring things we know together to create a new combination so much faster and better than humans. But, humans will be needed to assess whether the new combination makes sense to solve problems humans want to solve. Creative ideas gain in value when they become meaningful to people and therefore human supervision as the final step in the creativity process will be needed.

One of the concerns we have today is that machines are not reducing inequality but enhancing it.

Let me illustrate this point with the following example: Experiments have been conducted where AI was given several ingredients to make pizzas, and some pizzas turned out to be attractive to humans, but other pizzas ended up being products that humans were unlikely to eat, like pineapple with marmite. Marmite is popular in the U.K. and according to the commercials, people love it or hate it, so, its a difficult ingredient. AI, however, does not think about whether humans will like such products or find them useful it just identifies new combinations. So, the human will always be needed to determine whether such ideas will at the end of the day be useful and regarded as a meaningful product.

AIB: What are the limits to management by algorithm?

De Cremer: When we look at it from the narrow point of view of management, there are no limits. I believe that AI will be able to do almost any managerial task in the future. That is because of the way we define management as being focused on the idea of creating stability, order, consistency, predictability, by means of using metrics (e.g., KPIs).

AIB: How can we move towards a future where algorithms may not lead but still be at the service of humanity?

De Cremer: First, all managers and leaders will have to understand what AI is. They must understand AIs potential and its limits where humans must jump in and take responsibility. Humanity is important. We have to make sure that people not only look at technology from a utility perspective, where it can make a company run more efficiently because it reduces cost by not having to hire too many employees or not training people anymore to do certain tasks.

I would like to see a society where people become much more reflective. The job of the future may well be [that of] a philosopherone who understands technology, what it means to our human identity, and what it means to the kind of society we would like to see. AI also makes us think about who we are as a species. What do we really want to achieve? Once we make AI a coworker, once we make AI a kind of citizen of our societies, I am sure the awareness of the idea Us versus them will become directive in the debates and discussions of the kind of institutes, organizations and society we would like to see. I called this awareness the new diversity in my book. Humans versus non-humans, or machines: It makes us think also about who we are, and we need that to determine what kind of value we want to create. That value will determine how we are going to use our technology.

One of the concerns we have today is that machines are not reducing inequality but enhancing it. For example, we all know that AI, in order to learn, needs data. But is data widely available to everyone or only a select few? Well, if we look at the usual suspects Amazon, Facebook, Apple and so forth we see that they own most of the data. They applied a business model where the customer became the product itself. Our data are valuable to them. As a result, these companies can run more sophisticated experiments, which are needed to improve our AI which means that technology is also in the hands of a few. Democracy of data does not exist today. Given the fact that one important future direction in AI research is to make AI more powerful in terms of processing and predicting, obviously a certain fear exists that if we do not manage AI well, and we dont think about it in terms of [whether] it is good for society as a whole, we may run into risks. Our future must be one where everyone can be tech-savvy but not one that eliminates our concerns and reflections on human identity. That is the kind of education I would like to see.

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Artificial Intelligence Will Change How We Think About Leadership - Knowledge@Wharton