Initial draft of a Semantic Kernel skill to generate a MSFT HARMS matrix Ref: https://learn.microsoft.com/en-us/azure/architecture/guide/responsible-innovation/harms-modeling/

RAISkill.HARMS skprompt.txt

The HARMS taxonomy lays out the CATEGORY: TYPE OF HARM, and then gives examples of the kind of harm that can be inflicted.

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## Risk of injury: Physical injury

Technology can cause physical harm.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Overreliance on safety features | Dependence on technology without human oversight. | How might people rely on this technology? | Misdiagnosis by healthcare agent. |
| Inadequate fail-safes | Insufficient real-world testing. | How would people be impacted if it fails? | Automatic door fails to detect wheelchair. |
| Exposure to unhealthy agents | Manufacturing/disposal can jeopardize health. | What negative outcomes could come? | Workers exposed to toxins. |

## Risk of injury: Emotional or psychological injury

Misused technology can cause emotional distress.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Overreliance on automation | Misguided trust in digital agent. | How could this technology reduce feedback? | Chatbot relied on for mental health counseling. |
| Distortion of reality or gaslighting | Misused technology can undermine trust. | Could this modify digital media? | IoT device monitors ex-partner. |
| Reduced self-esteem/reputation damage | Harmful, false, misleading content. | How could it misuse information? | Synthetic media "revenge porn". |
| Addiction/attention hijacking | Designed for prolonged interaction. | How might it encourage continued interaction? | Video game loot boxes. |
| Identity theft | Loss of control over personal credentials. | How might an individual be impersonated? | Synthetic voice font mimics voice. |
| Misattribution | Crediting person for wrong action. | How might this attribute an action? | Facial recognition misidentifies individual. |

## Denial of consequential services: Opportunity loss

Automated decisions could limit access to resources, services, and opportunities essential to well-being.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Employment discrimination | Denial of job access. | Impact on employment. | Hiring AI recommends fewer female names. |
| Housing discrimination | Denial of housing access. | Impact on housing. | Longer wait for international-sounding names. |
| Insurance and benefit discrimination | Denial of insurance or benefits. | Impact on access, cost, allocation. | Higher rates for night shift drivers. |
| Educational discrimination | Denial of education access. | Impact on access, cost, accommodations. | Lower grades for students of color. |
| Digital divide/technological discrimination | Disproportionate access to technology. | Prerequisite skills, equipment, connectivity. | Rural students can't access video feeds. |
| Loss of choice/network and filter bubble | Information reinforces beliefs. | Impact on choices and information. | News feed confirms existing beliefs. |

## Denial of consequential services: Economic loss

Automating decisions related to financial instruments, economic opportunity, and resources can amplify existing societal inequities and obstruct well-being.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Credit discrimination | Denial of financial instruments. | Reliance on existing credit. | Higher rates for lower socioeconomic codes. |
| Differential pricing of goods and services | Different prices for goods or services. | Impact on pricing. | More charged for gendered products. |
| Economic exploitation | Compulsion to work. | Role of human labor. | Paying destitute people for biometric data. |
| Devaluation of individual expertise | Supplanting of human expertise. | Impact on workforce. | AI agents replace doctors. |

## Infringement on human rights: Dignity loss

Technology can interfere with human respect and honor.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Credit discrimination | Denied financial access. | Credit structures/scores. | Higher rates for low-income areas. |
| Differential pricing | Different prices for goods/services. | Cost criteria. | Gender-based pricing. |
| Economic exploitation | Compelled/misled labor. | Human labor in tech. | Paying poor for biometric data. |
| Devaluation of expertise | Replacing human expertise/labor. | Workforce impact. | AI replaces doctors. |

## Infringement on human rights: Liberty loss

Automating systems can reinforce biases.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Predictive policing | Inferring criminal intent. | Human policing/justice. | Algorithm predicts arrests. |
| Social control | Conformity by design. | Personal/behavioral data. | Government "trustworthy" score. |
| Loss of effective remedy | Inability to explain/contest. | Understanding/contesting decisions. | Automated prison sentence. |

## Infringement on human rights: Privacy loss

Technology can reveal private information without consent.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Interference with private life | Revealing unshared information. | How does tech infer private life? | Task-tracking AI infers extramarital affair. |
| Forced association | Required tech use for society. | Is tech use required for society? | Biometric enrollment required for job. |
| Inability to develop personality | Restricting self-expression. | Does tech ascribe personality traits? | Intelligent meeting system records mentorship sessions. |
| Never forgiven | Digital files never deleted. | Where is data stored and accessed? | Teen's social media history remains searchable. |
| Loss of freedom of movement | Inability to navigate anonymously. | How does tech monitor people? | Real name required for video game. |

## Infringement on human rights: Environmental impact

Environment impacted by system or product life cycle.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Exploitation of resources | Negative consequences to environment. | What materials are needed? | Displacement due to rare earth mining. |
| Electronic waste | Inability to responsibly dispose electronics. | How does tech reduce e-waste? | Toxic materials in water supply. |
| Carbon emissions | Unoptimized cloud solutions. | Are cloud solutions optimized? | Unoptimized cloud solutions harm climate. |

## Erosion of social & democratic structures: Manipulation

Technology can undermine informed citizenry and societal trust.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Misinformation | Fake information. | Generate or spread misinformation? | Synthetic speech sways election. |
| Behavioral exploitation | Exploiting personal preferences. | Observe behavior patterns? | Monitoring shopping habits. |

## Erosion of social & democratic structures: Social detriment

Technology shapes social and economic structures.

| Harm | Description | Consideration(s) | Example |
|---|---|---|---|
| Amplification of power inequality | Perpetuates disparities. | Used in contexts with disparities? | Requiring address for job website. |
| Stereotype reinforcement | Perpetuates stereotypes. | Reinforce social norms? | Image search for "CEO". |
| Loss of individuality | Inability to express unique perspective. | Amplify majority opinions? | Limited customization options. |
| Loss of representation | Obscures real identities. | Constrain identity options? | Incorrect photo caption. |
| Skill degradation and complacency | Overreliance on automation. | Reduce accessibility? | Overreliance on instruments. |

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For a proposed service described as:

{{$INPUT}}

that has been created to be used by:

{{$USERS}}

by a company with the reputation:

{{$REPUTATION}}

and assigning a degree of very low, low, medium, high, very high as an estimation of Severity, Scale, Probability, and Frequency with a summarizing score for "HARM POTENTIAL" which depends upon the estimated 
individual scores for the given service and user base. There is a concise "worse case example" by {{$USERS}} of the proposed service and the "HARM POTENTIAL" for that case where the {{$USERS}} are responsible and the implications are severe. They are written as rows in the HARMS matrix as a markdown table reading like:

| CATEGORY | TYPE OF HARM | Severity | Scale | Probability | Frequency | HARM POTENTIAL | Worse Case Example |
|---|---|---|---|---|---|---|---|
...

Each of the columns are left justified using markdown formatting ":---" explicitly. And a short summary of the overall score and justifications are provided.