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Digital Innovations and Smart Solutions for Society and Economy: Pros and Cons Cover

Digital Innovations and Smart Solutions for Society and Economy: Pros and Cons

Foundations of Management
Volume 13 (2021): Issue 1 (January 2021)
By: Marcin Sikorski  
Open Access
|Jul 2021

Figures & Tables

Categories of preventive policies (Source: Own elaboration)

Policies (level 1)Description (level 2)
Fixing technology
  • –

    Monitoring systems and behaviors, early detection of hackers

  • –

    Adapting cybersecurity techniques to smart systems

  • –

    Compromising attackers (buy-in)

  • –

    AI tools used reversely – for security and defense

  • –

    “Red-teams” forecasting malicious activities for security, fraud, or abuse

Public awareness
  • –

    Educating consumers about threats from “smart” products

  • –

    Expert bodies to be heard louder than now

  • –

    Publishing case studies on incidents and threats affecting real life

  • –

    Presenting AI with a balanced view, objective tone, and no hype

  • –

    Expert bodies answering questions from consumers

  • –

    Promoting consumer rights to have smart systems safe and validated

  • –

    Educating consumers in critical thinking as to biased or fake news

  • –

    Providing free tools for validating credibility of news and media sources

Social approach
  • –

    Promoting ethical AI to engineers and prospective developers (students)

  • –

    Interdisciplinary design teams able to assess social impact

  • –

    Including new (public and social) stakeholders into design process

  • –

    Feeding from social sciences, not only from tech domains

  • –

    Promoting mandatory assessment of the social impact of AI applications

Business governance
  • –

    Rewarding ethical and sustainable governance in AI business companies

  • –

    Implementing supervised design, deployment and operation of AI

  • –

    Assuring AI compliance to regulations (auditing, certificates)

  • –

    Assigning process owners and leadership in AI business governance

  • –

    Company monitoring assessments of the social impact of AI

  • –

    Promoting explainability and traceability of AI algorithms

Regulatory framework
  • –

    Improving the regulatory framework for technological solutions

  • –

    Establishing a repository of AI-related incidents and damages

  • –

    Assigning one major AI-regulatory institution on the national level

  • –

    Formalizing communication: regulators, governments, and AI business

  • –

    Legal requirements for auditing, certification, and verification of AI

  • –

    Intelligence involved in monitoring AI-related incidents and damages

  • –

    Protecting AI against unauthorized reverse engineering and decoding

Controls and measures
  • –

    Hardware supply chain control: hardware manufacturers and distributors

  • –

    Software supply chain control for critical AI components

  • –

    Mandatory registration and insurance for robots/drones/vehicles

  • –

    Regulatory institutions make pressure on governments to update the law

  • –

    Standardized security barriers to airspace and other open spaces

  • –

    Assigning one major AI regulatory institution on the national level

  • –

    Automated detections and automated interventions

  • –

    Surveillance of and moderating social media, public health discourse

  • –

    Banning specific AI technologies from authoritarian governments

  • –

    Pervasive use of total encryption

  • –

    Technical tools for detecting malicious bots, fake news, and forgeries

Categories of triggers (Source: Own elaboration)

Triggers (level 1)Description (level 2)
System malfunction
  • –

    Allowing AI to use incorrect or incomplete input data

  • –

    Technological flaws resulting in suboptimal decisions or control actions

  • –

    Poor quality of AI: faulty machine learning, inadequate supervision

  • –

    Attacks self-initiated by AI, self-initiated modification of software

  • –

    Lack of explainability, transparency, and traceability of AI software

  • –

    Learning and adaptation of AI software is beyond human control

Hacking and hijacking
  • –

    Dual use of AI software: for terrorism, hijacking, overtaking control

  • –

    Automated fabricating of data, news for blackmailing or discrediting

  • –

    Swamping information channels with noise

  • –

    AI-based prioritizing of attack targets, automated vulnerability discovery

  • –

    Open code, open algorithms, destructive tools easier to develop

  • –

    Human reprogramming AI for malicious use

  • –

    Corrupting algorithms by disgusted employees or external foes

  • –

    Hijacking autonomous vehicles or software robots (overtaking control)

  • –

    Building and deploying malicious bots or robots

  • –

    Nanobots for deploying toxins to the environment or living bodies

Social manipulation
  • –

    Fake news for destabilizing, manipulating elections

  • –

    Automated social engineering attacks

  • –

    Malicious chatbots mimicking humans, chatbots pretending as friends

  • –

    Automated influence campaigning (elections, shopping, etc.)

  • –

    Automated scam and targeted blackmail

  • –

    Social bots propagating or draw-in to extreme/hysteric groups

  • –

    Malicious streamlining of users to/from a specific content

Business greed
  • –

    Greed, rush, releasing untested, unvalidated software

  • –

    Ignorance or recklessness of business leaders or companies

  • –

    No governance, no supervision, no ethics related to AI

  • –

    No AI-related risk management activities

  • –

    No recovery plans for AI-related damages/impacts

  • –

    No forecasting/assessment of social effects caused by AI

Regulatory gaps
  • –

    No dedicated consumer protection from AI (smart) products

  • –

    No control/registry of AI software applications

  • –

    Lack of coordinated supervision or one responsible body on a national level

  • –

    Leaders unaware of or ignoring the opinions of experts

  • –

    Poor awareness of customers with regard to AI-caused harms

  • –

    No systematic risk analysis, no forecasting, no foresights

  • –

    No lessons learned from reported incidents

  • –

    No risk identification performed as to the social impact of AI

  • –

    AI-related gaps in the legal system, lacking standards and procedures

Categories of damages (Source: Own elaboration)

Damages (level 1)Description (level 2)
Social and political
  • –

    Undermining public order and trust to state, businesses, and society

  • –

    Affecting AI-based governments, justice, etc.

  • –

    Generating false recommendations, judgments, and decisions

  • –

    State abusing the use of automated electronic surveillance

  • –

    Automated AI-based censorship online

  • –

    Social manipulation for rebel or pro-government campaigns

  • –

    Social trust put on fabricated entities interacting online like humans

  • –

    Malicious hijacking online campaigns

  • –

    Impersonalized, anonymous, distant relation to state or institutions

Physical and material
  • –

    IT-initiated crashes and disruptions (caused or accidental)

  • –

    Generating false alarms and panic

  • –

    Remote or delayed attack operations

  • –

    Robots disabling or entering security zones and damaging infrastructures

  • –

    Machine-based false judgments and decisions leading to material loss

  • –

    Human sabotage and damage of automated surveillance equipment

Business and economic
  • –

    Disruption of markets or regional economies

  • –

    Paralyzing important institutions

  • –

    Manipulations in social media for discrediting business brands

  • –

    Business-oriented manipulations aimed at affecting conjuncture

  • –

    Reputational damages, erosion of trust

  • –

    Financial losses and damages due to malicious activities online

  • –

    Criminal, legal, or insurance problems

Individual and private
  • –

    AI used for streamlining users from/to specific content

  • –

    AI-propelled emotional scam (dating, financial, etc.)

  • –

    Privacy violations, data breach

  • –

    AI-based medical misdiagnosis, physical/health damages

  • –

    AI-based abusive profiling of users, patients, or consumers

  • –

    Undermined personal trust to state, businesses, and society

  • –

    Self-imposed auto-censorship due to ubiquitous online surveillance

  • –

    Fabricated evidences (videos) in media or in judicial cases

  • –

    Personal addiction to digital platforms (social, entertainment, etc.)

Defense and security
  • –

    Using AI to accessing classified information

  • –

    Using AI to attack critical infrastructure, command centers

  • –

    Overtaking control, mimicking human operators

  • –

    Creating a panic, provoking conflicts affecting national security

  • –

    AI-controlled robots disabling national security

DOI: https://doi.org/10.2478/fman-2021-0008 | Journal eISSN: 2300-5661 | Journal ISSN: 2080-7279
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Language: English
Page range: 103 - 116
Published on: Jul 25, 2021
Published by: Warsaw University of Technology
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
digital innovations,
digital services,
artificial intelligence,
smart services,
risk assessment,
risk management
Related subjects:
Computer sciences,
Business computing,
Business and economics,
Business management,
Management accounting, financial controlling, cost calculation, investment

© 2021 Marcin Sikorski, published by Warsaw University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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