IDSC Test G -Topics 13 & 14

human bias

• how much data we can process

• how much data is being input

• how much memory we can store

visual bias (optical illusions)

• object recognition (lateral inhibition): see contrast and gives extra attention to edges

• visual system expects a single lighting source

• face perception - eyes & mouth for expression analysis, lips for speech analysis, features for name recognition

Brain challenges

1. How to process the massive amount of sensory input data the brain receives every moment

2. How to categorize and structure information for fast and accurate retrieval

3. how to act fact

4. The struggle of finding meaning (even if it’s not there)

• Evolution’s solution = bias!

  • over 180 different types of cognitive bias

challenges that bias addresses

• memory bias

• lack of meaning

• time pressure

• information overload

why understanding bias is important (WW2)

survival deviation: focusing only on successful or surviving cases, while ignoring failures

• only focused on planes that returned = draw wrong conclusions

• important data was missing (planes that didn’t return)

heuristics

The way humans use bias, tendencies, and shortcuts to make decisions

• Understanding these shortcuts can aid in design

  • can apply to physical product design, store layout, POS/kiosk devices, website design

How orgs use bias (heuristics)

choice architecture: the design of environments (physical and digital) that influences how people make decisions

• availability- use easily accessible info to create judgments

• social proof - recommendations and reviews influence

• endowed progress effect - more motivated to finish something if they feel they’ve already made progress (even if given artificially)

• anchoring - high initial price to make a discounted price attractive

• scarcity - only 2 tickets remain at this price

• framing - price/color may affect perception of quality/value

• priming - something you just saw/heard unconsciously influences how you think or respond next

skinnerbox (gamification)

the anticipation of a reward, not the reward itself that triggers the brain

• random rewards were more effective than consistent rewards

• dopamine responds more to uncertain rewards

loot boxes (gamification)

rewards with random values

• incentives

• randomness

• scarcity (limited time)

• collection (needed for completion, “I need the full set”)

similar to gambling

how orgs use bias (gamification)

• loss aversion: losses are more psychologically powerful than gains

• gambler’s fallacy: an event is more or less likely, given a previous series of events

• sunk cost fallacy: prior investments justify further expenditures

• illusion of control: tendency for people to overestimate their ability to influence outcomes

societal issues that bias can create

• statistical bias - sampling error, improper techniques

• conflicts of interest - funding bias, regulatory issues, favoritism

• contextual bias - media, academics

• prejudices - racism, sexism, classism

how to address bias as individuals

• self-reflection and correction

• understand your bias, prejudice, and stereotype (Harvard bias tool)

• reduce your bias - diverse edu, travel, interaction

technology bias dichotomy

• reduce human bias - proper use of data, statistics, machine learning

  • inadvertently scale and magnify bias

prompt-injection

compromising generated AI by entering prompts that cause it to behave in unintended ways

data poisoning

deliberately feeding incorrect data to an AI so that it generates incorrect results

algorithmic bias

Bias in:

• recruitment tools

• word associations

• online ads

• generative AI

to eliminate bias

good experiment design

• randomization

• (double) blind

• positive and negative controls (placebo)

• statistical replication

ethics of behavioral designs

• support customer goals

• provide transparency

• use for persuasion, not coercion (moral autonomy)

• consumers nudge themselves

• use gamification for consumer engagement, not sales generation

• align with social good

issues posed by e-waste

• high volumes

• toxic

• poor design and complexity

• labor issues

• financial incentives

• lack of regulation

Green IT

using computing resources in ways that reduce environmental impact

• use and disposal of tech hardware

• Energy Star program: standards for computers and servers

• right to repair: proposed legislation and Apple reversa

energy star prgram

standards for computers and servers

how managers can deal with obsolete tech

• audit disposal and recycling partners

• certify as ISO compliant to attest to quality management and environmental processes

• certify firms via a third-party network

ITIL

IT Infrastructure Library

• covers best practices for delivering IT services