Trust in Data In High-Risk Decision-Making – Human Factors (Part 2)

Trust in Data

In High-Risk Decision-Making – Human Factors (Part 2)

- Dr. Marty Trevino, Chief Scientist, OptimEyes.ai

In this second part of Data Trust in High-Risk Decision-Making, we explore beyond the common beliefs and precepts around how the brain makes data-informed decisions.?

INTRODUCTION

Understanding how our brains make decisions from data and contextual information is among the most difficult endeavors ever undertaken by science. ?An important subset of this scientific exploration to cyber, OT and enterprise risk professionals is how the brain makes data informed high risk decisions. At the tip of this exploration are bodies of knowledge of Psychology, Biology, and of note - the Cognitive Neuroscience of Risky Decision-Making.

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Understanding how the brain makes data-informed decisions is not an esoteric or theoretical exercise.?Understanding how we make decisions from data is enabling a host of functions from technology selection to capability implementation. This understanding will underpin the next generation of human/computer complementarity for productivity, design and innovation.?The idea of complementarity in the form of ‘human-centric’ interface design is not new but it is only now being grounded in science vs beliefs.??We can now specifically design User Interfaces, Dashboards, and even individual Visual Analytics to create higher degrees of resonance by targeting the brain’s right hemisphere and the brain’s System 2 decision making processes.[1]?The Integration of Cognitive Neuroscience into IT, OT and DX to improve strategic decision-making has immense potential to contribute to operational efficacy, uniqueness, and competitive advantage.?

THE HUMAN BRAIN & DATA DECISIONS

There has been a single overarching error in virtually all efforts to improve data-driven decision-making; that is, to approach the challenge as a technology, data, or visualization problem vs. a deep understanding of how the brain interrogates, trusts, and then makes decisions with data.

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To discuss this topic in depth would require multiple dissertations, but we can simplify some of the intractable functioning of the brain when analyzing data and apply this understanding to make immediate operational impacts in Enterprise and Cyber Risk.?As we attempt to “bake in” data informed decision-making into our organizational ‘DNA’ we must first begin with the following cognitive truths:

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• Every person interrogates data differently.[2]

(Areas of focus and duration of examination vary greatly between

individuals.?Thus our ‘outcomes’ and ‘takeaways’ can also differ.)

(The brain examines data based on unconscious; desired, predetermined

outcomes intended to validate our presuppositions.[3]?)

• We ignore and dismiss data that does not agree with our mental models and presuppositions.
• The brain will almost always select System 1 vs 2 when making decisions.[4]
• The brain uses 6x more information from what it believes to be true vs what it is seeing with its eyes when updating its mental model.
• Data examination and decision-making is imperfect, and we are all prone to decision errors ranging from Framing to Optimism bias to WYSIATI and many more.[5]

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“We’ve greatly increased the color options for charts on our dashboards.?This will improve decision making.

-Senior Sales Executive - Self Service BI Company

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It Did Not…”        

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COMPLEMENTARITY OF DESIGN

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When system designers talk about “Human Centric Design” and enabling “Humans to do what they do best and Computers to do the same” most have little idea beyond simplistic tenants of what such as statement implies. In designing next generation analytics and AI for high-risk decisions, we must move beyond simplistic tenants such as “computers analyze massive data well and our brain does not”.?In the Enterprise Risk, IT/OT and Cyber security realms we can now conceptualize Human/Computer Complementarity to enable data interrogation and exploration by a working pair of human user and ‘mated’ AI.[6]?This conceptualization originates from a Cognitive Neuroscience foundation and is intended to function as a sort of augmentation to the user.?An example of this form of Complementarity is the structuring of novel user interfaces and analytics to enable the brain’s hemispheric functions, maximize short term and working memory, force the use of system2 and create higher degrees of resonance.?Specific tools and techniques are emerging that are of immediate use, these include dimensional scaffolding (borrowed from Biology and the medical field) and 2- and 3-dimensional geo-temporal exploration.?The latter of which is intended to enable a form of storytelling while dealing with the complex issues of dimensionality, time and our limited memory.

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This latest treatment of Complementarity with a cognitive neuroscience basis extends far beyond what is currently fielded and warrants extensive exploration by every corporation as the outcomes will almost certainly be increased productivity, performance, and innovation. ?Next generation analytic interfaces will dynamically design and update the visual and temporal elements of data/contextual information to the unique user based and or persona, “day-in-the-life” profiles, job role, industry vertical and peer behaviors.?Individually specific AI will algorithmically recommend, highlight data elements for inspection, aggregate and separate contextual information according to priority/relevance and highlight this to the user with temporal specificity. In addition, the system will generate specific Visual Analytics to which the user has shown preference for, such as Juxtaposing vs Time Sequencing of data. ?Complementarity is designed to engender trust in data as well as work ‘with’ the brain in its decision processes, intractable elements and how it perceives data/information. ?The aspect of trust poses fascinating challenges as the latest research shows that trust in technology and data is formed in vastly different ways than trust in people.

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ZERO TO ONE [7]

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As we conceptualize and design the next generation of analytic systems we do so from the new lens of Complementarity and an inside out, human centric lens. ?As enterprise risk has risen, cyber insurance and other costs spiral upward it is not enough to simply make incremental improvements in our system designs to improve decision-making. ?Thus, the question becomes can we create true disruption in the best sense by altering our approach to data-informed decision-making at the highest levels? In the next series of compositions, we will explore the application of Multi-Modal Cognition as a core element of next generation analytic interfaces and decision-making.

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Dr. Marty Trevino

Chief Scientist

[1] Reference to Daniel Kahneman’s groundbreaking work.

[2] This is huge because it changes the unit of measure from X to 1.?It forces us to focus on enabling each individual decision-maker vs building generic systems/interfaces. I.E., – no two CFO’s or CISO’s are identical in their exploration of data, informing their mental models and determining action based on data.

[3] See the research of Russian Psychologist Alfred Yarbus

[4] According to Kahneman System 1 is rapid, heuristic based an largely unconscious – it is both accurate and prone to error.?System 2 is slow, requires more energy, is a detail-oriented examination of in our case data/analytics or information regarding a high-risk topic.

[5] WYSIATI – What You See Is All There Is (Daniel Kahneman)

[6] Specific to the User, persona and domain.

[7] A reference to the excellent book by Peter Thiel and Blake Masters.