Let's present the risks associated with Generative AI based on their alignment with the HLEG principles and AI lifecycle stages.

$1.$ Human Agency and Oversight:

Deployment:

R$7$: Automation Bias $($Over$-$reliance on AI systems without human judgment$).$ DO LIKE THIS

R$11$: Dependence on the Model Developer$/$Operator $($Lack of transparency and control over AI behavior$).$

R$20$: Lack of Human Control $($AI systems making decisions without human intervention$).$

Monitoring$/$Feedback:

R$7,$ R$11,$ R$20($Same as above, emphasizing the need for ongoing human supervision and intervention$).$

$2.$ Technical Robustness and Safety:

All Stages:

R$2$: Lack of Quality, Truth, and Illusions $($Hallucinations and inaccurate outputs$).$

R$4$: Lack of Reproducibility and Explainability $($Difficulty understanding and reproducing AI decisions$).$

R$5$: Lack of Security of Generated Code $($Vulnerabilities in AI$-$generated code$).$

R$6$: Incorrect Response to Specific Inputs $($AI failures in understanding and responding appropriately$).$

R$8$: Vulnerability to Interpreting Text as a Command $($AI susceptibility to malicious prompts$).$

R$10$: Self$-$Reinforcing Impacts and Model Collapse $($AI models degrading over time due to feedback loops$).$

R$20$: Lack of Human Control $($AI systems operating without sufficient safeguards$).$

R$22$: Homograph Attacks $($Exploiting similar$-$looking characters to deceive AI$).$

R$23,$ R$24,$ R$25$: Prompt Injection Attacks $($Manipulating AI behavior through malicious input$).$

Data Collection$/$Preprocessing:

R$26$: Data Poisoning Attacks $($Introducing malicious data to corrupt AI training$).$

Model Training:

R$15$: Knowledge Gathering and Processing in the Context of Cyber Attacks $($AI misuse for information gathering in cyberattacks$).$

R$16$: Malware Creation and Improvement $($AI used to generate or enhance malicious code$).$

R$19$: Attackers can reconstruct a model's training data via targeted queries in LLM $($Privacy and security breach$).$

R$27$: Model Poisoning Attacks $($Subtle modifications to training data to compromise AI models$).$

R$28$: Learning Transfer Attacks $($Transferring malicious knowledge from one model to another$).$

Deployment:

R$15,$ R$16,$ R$17($Malware Creation, Improvement, and Placement$).$

R$18$: RCE $($Remote Code Execution$)$ Attacks $($Exploiting AI vulnerabilities for unauthorized control$).$

$3.$ Privacy and Data Governance:

Data Collection$/$Preprocessing:

R$9$: Lack of Confidentiality of Input Data $($Inadequate protection of user data$).$

R$14$: Re$-$Identification of Individuals from Anonymous Data $($Revealing personal information from anonymized data$).$

R$26$: Data Poisoning Attacks $($Compromising data integrity and privacy$).$

Model Training:

R$9,$ R$14($Same as above$)$

R$19$: Attackers can reconstruct a model's training data via targeted queries in LLM $($Privacy violation through data extraction$).$

$4.$ Transparency:

All Stages:

R$4$: Lack of Reproducibility and Explainability $($Difficulty understanding AI decision$-$making$).$

Model Training:

R$19$: Attackers can reconstruct a model's training data via targeted queries in LLM $($Lack of transparency in data usage$).$

Deployment:

R$12$: Misinformation $($Fake News$)($AI$-$generated misleading or false information$).$

$5.$ Diversity, Non$-$discrimination, and Fairness:

Data Collection$/$Preprocessing and Model Training:

R$1$: Undesirable Results, Literal Memory, and Bias $($Harmful or biased content due to training data or limitations$).$

R$5$: Lack of Security of Generated Code $($Security vulnerabilities that can disproportionately affect certain groups$).$

R$6$: Incorrect Response to Specific Inputs $($Biased or unfair responses due to flawed training data$).$

$6.$ Societal and Environmental Well$-$being:

Deployment:

R$12$: Misinformation $($Fake News$)($Negative societal impact of AI$-$generated false information$).$

R$17$: Malware Placement $($AI$-$facilitated spread of malicious software$).$

R$18$: RCE $($Remote Code Execution$)$ Attacks $($AI$-$enabled cyberattacks causing harm$).$

$7.$ Accountability:

All Stages:

R$11$: Dependence on the Model Developer$/$Operator $($Lack of clarity on responsibility for AI actions$).$

Deployment:

R$13$: Social Engineering $($AI used to manipulate individuals$).$

R$20$: Lack of Human Control $($Unclear accountability for AI$-$generated outcomes$).$

Note: Some risks, like R$2($Lack of Quality$)$ and R$4($Lack of Reproducibility and Explainability$),$ are relevant across multiple stages and principles.

I hope this categorization helps in understanding the complex landscape of Generative AI risks! DO GRAPHICAL SCHEME FOR AI LIFECYCLING FOR EACH RISKS AND A GRAPHICAL SCHEME FOR EACH RISKS ACCORDING TO $7$ PRINCIPLES ACROSS THE LIFECYCLE DO LIKE THIS