YOURSTORY & AIQRATE jointly unveiled a bespoke report on Data Engineering 4.0
Data Engineering has come out as a prominent area within the AI arena; building robust data pipes has assumed significant importance to fuel machine learning algorithms. At Future of Work 2020 event, YOURSTORY & AIQRATE jointly unveiled a bespoke report on “Data Engineering 4.0: Evolution, Emergence & Possibilities in the Next Decade”.
The report identifies 5G, Data Pipeline+, AI Managed Data Lakes, Edge Cloud and IoT as some of the main drivers for the emergence of Data Engineering 4.0 to realize the much anticipated cross industry collaboration enabled business models involving Smart Cities, Autonomous Vehicles, and Smart Factories.
3 Ways To Human Centric AI
Three Ways to Facilitate a Symbiotic Relationship Between Cognitive Intelligence and Behavioral Sciences
After every conference I speak at about the transformative power of Artificial Intelligence and its potential to unlock untold business value, the one question that often crops up from the audience is.. if AI is expected to perform much of the grunt work in the enterprise world, what scope is there for the so-called ‘human’ qualities?
Is the future of business and technology so deeply intertwined that it leaves virtually no scope in the future for the vagaries of human intelligence and behavior? The answer is quick and simple – absolutely not. Artificial Intelligence, while a great paradigm-shifter in the world of business, is still one of the tools that will be used by humans for making better decisions. At the end of the day, AI will still be developed and used by humans. And maintaining the ‘human element’ in the way it is made, delivered, used and improved will most certainly make it a lot more successful. AI exists to make human life simpler and richer – and hence it is critical that AI practitioners and data scientists adopt a human-centric approach to its development, deployment and adoption. Even the best AI will become quickly redundant without inputs from real humans on how to accelerate strategic decisions and processes.
How do we then build in that ‘human element’ into our Artificial Intelligence tools? This is where humanities-centric subjects of design and behavioral science come into the picture. What is behavioral science? Simply put, it is the study of internal cognitive processes of humans and societies and how these processes manifest into external perceptible and imperceptible actions and interactions. Behavioral science typically stands at a nexus of various subjects – borrowing aspects from sociology, anthropology, psychology and even economics and political science. Its interdisciplinary nature precludes the scale of impact it can have if applied correctly. In technology, and specifically in AI, behavioral science will and should impact how we build, use and interact with technology.
I see primarily three key areas where the symbiosis of AI-led cognitive intelligence systems and behavioral science can unlock massive value for enterprises that marry these two starkly different, but extremely complementary fields of study:
Appeal to the Non-Conscious
We have known for nearly a century now that a large majority of human biases, inferences, preferences and reactions are largely controlled by the dark recesses of our non-conscious brain. For technologists to build successful AI products, that are widely adopted and used they need to reach out inside the non-conscious parts of the human brain and orchestrate responses from there.
In AI technology specifically, user adoption is often the difference between make and break for products. Numerous AI products are mostly informed by the data they gather from human actions and their preferences. This data feeds the algorithms running in the background and makes them more sophisticated to better understand their human overlords. To that end, AI products need to have a strong underpinning in behavioral science, so that they can appeal to the non-conscious and improve adoption.
Take for example the work done by Nir Eyal for his book, ‘Hooked: How to Build Habit Forming Products.’ In the book, Eyal writes about multiple ways in which human subjects get applied to technology development. One of them is the Hook canvas – a loop comprising triggers, actions, rewards and investments – which are the cornerstone features of every addictive software you’ve used – from Facebook to Instagram, Snapchat and YouTube. Another is the idea of using the trinity of emotion, features and incentives – extremely relevant ideas to anyone working on building AI products. Another example comes from Worxogo – an Indian startup that employs behavioral design, neuroscience in tandem with predictive analytics to enhance employee performance through nudges to the non-conscious.
Build with Humans
Not only is AI built to serve humans, it is also built by humans. To that end, it becomes extremely important to consider what emotional triggers help define what we build and how we build it. Again, behavioral science practitioners have a key role to play in order to engage empathy in defining the requirements and going about the development of AI. Learnings from behavioral science can bring to light immeasurably important interventions for how we manage and lead teams, collaborate between a team and across multiple teams – all while maintaining a high level of motivation by appealing to a higher sense of purpose. It is worth examining how something as simple as empathy can be extremely valuable in how we build software. For instance, with improved self-awareness and empathy, developers can feel an intrinsic desire to write cleaner code while maintaining proper documentation. Also, given that AI is largely deployed using the DevOps methodology – empathy can be the difference between whether we can build a trust-based bridge between how we build, deploy and automate releases faster.
Beyond the ‘how’ of AI development, behavioral science can also contribute meaningfully to the ‘what’. Currently a lot of concern around AI is related to ethics – will AI lead to loss of meaningful work for humans? What data privacy issues can rear their head when we deploy large-scale data capture systems to improve our algorithms? We need to move the dial from apathy to empathy in the process of conceptualizing software – and knowledge of behavioral science will undoubtedly help AI practitioners develop more responsible AI.
Artificial Emotional Intelligence
The third key application of behavioral science – and possibly the most game-changing of the lot is – how can we apply behavioral science to make our systems more ‘human’? Is it possible to add a dash of EQ to these high IQ systems?
I certainly think there is a huge scope for developing AI that has a strong human bent. Consider the applications we are building today with AI and robotics – companions for the elderly, coaching apps for autistic children, even something as comparatively mundane is chatbots for customer service. Behavioral science holds the key to achieving the holy grail of how we can better balance the human-machine equation, by infusing human qualities into artificial systems.
To enable this, it is important to know who we are building for and what are their intrinsic and non-conscious needs. Behavioral science holds the clues that can complement AI’s ability to eliminate biases, while serving the emotional needs of humans. For example, StressSense tracks when people are highly stressed and helps them avoid anxious situations. This kind of breakthrough research can help in multiple AI applications, teaching them how to behave with humans, while ensuring a strong impact.
As technology providers and businesses work together to build transformational artificial intelligence systems and data science teams, it is very important to consider the human element. These teams would do well to develop a better understanding of whom the AI is built for and how it is used – through techniques offered by behavioral science. Balancing the human-machine equation and powering a complementary relationship between AI systems and the people who use them necessitates an infusion of behavioral science into the process. Ultimately, for AI to succeed, we need both – the foresight of technology as well as the insight of humans.