Fair Technical Assessment/Technical Interview Framework

Author: Manuela Cortés Granados

Date: 14 February 2025, Bogotá D.C., Colombia (GMT -5)
Updates: 29 April 2025 (Articles 2.2.6 and 2.2.7)

He or she who prays the Holy Rosary of the Blessed Virgin Mary on a daily basis is able to distinguish Christian Divine Reality from satanic/luciferian illusion, the second one being ultimately responsible for practically all the evils such as conflicts in human society, addictions, mortal diseases, divorces, famines, wars, murders, violations of human rights, lack of freedoms, promotion of ignorance and hatred in the world. By praying the Holy Rosary, one can effectively fight against all or at least most of the seven deadly sins, including the most lethal of all, which is the deadly sin of Pride. However, this spiritual task is not complete without reading and fully understanding the document of the 33 petitions, which can be found in the following URL:

https://mcortesgranados.github.io/PETICIONES-SANTO-ROSARIO.pdf

As clearly stated in paragraph 19, page 10, Petition to the Holy Rosary A, once a person reads this message and its contents, he or she becomes spiritually accountable before God to seriously consider the decision of praying the Holy Rosary daily and reflecting deeply on the full content of the 33 petitions. If this call is embraced with sincerity, God's blessings will be abundant, not only for the individual but also for their family, friends, and acquaintances. This will bring immeasurable benefit to the human race, helping to prevent its downfall and destruction in the most literal and spiritual sense. Failure to do so—by ignoring this invitation or treating it with indifference—may result in the loss of divine blessings and the support of the Blessed Virgin Mary. In such a case, the person is left without angelic protection and becomes dangerously exposed to the direct attacks and deceptions of Satan. Satan is, in truth, the principal intellectual author behind all the evils that afflict humanity.

INDEX

• Introduction: Recognizing Human Dignity in Technical Assessments
  
• 1. Purpose & Objectives
  
  • Article 1.1:
  • Article 1.2:
  • Article 1.3
• 2. Core Principles of Fair Assessments
  • 2.1 Objective Criteria
    • Article 2.1.1
    • Article 2.1.2
    • Article 2.1.3
    • Article 2.1.4
  • 2.2 Transparency
    • Article 2.2.1
    • Article 2.2.2
    • Article 2.2.3
    • Article 2.2.4
    • Article 2.2.5
    • Article 2.2.6: Interview Report Transparency
      • 2.2.6.1. Purpose:
      • 2.2.6.2. Policy:
        • 2.2.6.2.1. Full List of Questions Asked
        • 2.2.6.2.2. Candidate’s Responses
        • 2.2.6.2.3. Evaluation Notes (Optional for Candidate)
        • 2.2.6.2.4. HR Transmission
        • 2.2.6.2.5. Rationale
    • Article 2.2.7: Candidate Right to Use Personal AI Notes or Record Session
      • 2.2.7.1. Purpose
      • 2.2.7.2. Policy
        • 2.2.7.2.1. Use of Personal AI Notes or Tools
        • 2.2.7.2.2. Right to Record Interview Session
        • 2.2.7.2.3. Evaluator Obligation
        • 2.2.7.2.4. Rationale
  • 2.3 Consistency
    • Article 2.3.1
    • Article 2.3.2
  • 2.4 No Bias or Unjust Practices
    • Article 2.4.1
    • Article 2.4.2
    • Article 2.4.3
• 3. Assessment Design Guidelines
  • 3.1 Defining Skill Requirements
    • Article 3.1.1. Clearly outline the skills being tested, such as:
  • 3.2 Avoiding Trick Questions
    • Article 3.2.1 Assessments should reflect real-world tasks and avoid impractical questions.
      
    • Article 3.2.2 Questions should allow candidates to demonstrate practical knowledge.
      
    • Article 3.2.3 Theoretical questions should be disclosed in advance, as the volume of potential topics is vast and unrealistic to memorize.
  • 3.3 Balanced Difficulty Levels
    • Article 3.3.1 Provide a mix of easy, moderate, and hard questions to evaluate all skill levels fairly.
      
    • Article 3.3.2 Ensure time constraints are reasonable and aligned with task complexity.
      
    • Article 3.3.3 Allow the use of documentation and reasonable online resources to reflect industry practices.
• 4. Evaluation Criteria
  • 4.1 Objective Scoring Rubric
    • Article 4.1.1 Code correctness: 40%
      
    • Article 4.1.2 Efficiency and performance: 20%
      
    • Article 4.1.3 Code readability and maintainability: 20%
      
    • Article 4.1.4 Problem-solving approach: 20%
  • Article 4.2 Automated Grading for Objectivity
    • Article 4.2.1 Utilize automated grading systems where possible to eliminate bias.
      
    • Article 4.2.2 If manual grading is required, it must follow predefined rubrics with documented justifications for deductions.
  • 4.3 Justification for Deductions
    • Article 4.3.1 Evaluators must provide clear explanations for score deductions.
      
    • Article 4.3.2 Vague feedback like "solution is not optimal" is insufficient; specific reasoning must be given.
      
    • Article 4.3.3 Disqualifying a candidate for missing a few advanced questions while correctly answering fundamental ones is unfair
• 5. Evaluator Responsibilities
  • Article 5.1 Evaluators must be trained on objective assessment methodologies.
    
  • Article 5.2 They must provide constructive, actionable feedback.
    
  • Article 5.3 They must not alter evaluation criteria mid-assessment.
    
  • Article 5.4 Personal biases should be actively avoided.
    
  • Article 5.5 Each question must be scored (1-10) with documented justification.
    
  • Article 5.6 Evaluators should self-assess their own grading for biases.
    
  • Article 5.7 Lengthy explanations that pressure candidates should be avoided, as they create an unfair psychological burden.
• 6. Unjust Practices in Assessments
  • 6.1 Forced Screen Sharing & Privacy Violations
    • Article 6.1.1 Mandatory screen sharing during coding assessments violates candidate privacy.
      
    • Article 6.1.2 Candidates should not be forced to use a webcam or share their entire screen unless necessary.
  • 6.2 Responsible Use of AI in Assessments
    • Article 6.2.1
    • Article 6.2.2
    • Article 6.2.3
    • Article 6.2.4
  • 6.3 Real-World Development Considerations
    • Article 6.3.1. Reasonable use of reference materials.
      
    • Article 6.3.2. Copying, pasting, and adapting existing code where appropriate.
      
    • Article 6.3.3. Open-book or open-resource formats where applicable.
      
    • Article 6.3.4. Use of tools like Chat GPT, AI hints, Google, Stack Overflow, and GitHub repositories.
      
    • Article 6.3.5. Use of an IDE or personal tools without assuming misconduct
• 7. Candidate Rights & Dispute Resolution
  • 7.1 Right to Know Scores and Feedback
    • Article 7.1.1. Candidates must receive detailed feedback, including:
  • 7.2 Right to Appeal
    • Article 7.2.1
  • 7.3 Right to Document the Assessment
    • 7.3.1 Candidates should have the right to
      • Article 7.3.1.1: Record the interview (audio-only) for transparency.
        
      • Article 7.3.1.2: Document all questions and answers.
        
      • Article 7.3.1.3: Provide their own self-evaluation of answers.
        
      • Article 7.3.1.4: Submit this documentation for HR review.
        
      • Article 7.3.1.5: Have their answers validated by an AI system to ensure fair grading.
• 8. Conclusion
  • Article 8.1: A fair technical assessment framework ensures evaluations are conducted transparently, objectively, and without bias.
    
  • Article 8.2: Companies must adopt standardized scoring criteria, avoid invasive monitoring practices, and allow candidates to appeal unfair judgments.
    
  • Article 8.3: By adhering to these principles, technical assessments can maintain integrity while ensuring that all candidates are evaluated fairly on their true merit.
    

Author: Manuela Cortés Granados

Date: 14 February 2025, Bogotá D.C., Colombia (GMT -5)
Updates: 29 April 2025 (Articles 2.2.6 and 2.2.7)

Introduction: Recognizing Human Dignity in Technical Assessments

Technical assessments/Technical Interviews are not merely tests of skill; they are a gateway to opportunity, physical survival in this world, and professional growth. Every candidate who undergoes these evaluations has invested years and money, often decades, with great expectations of a happy and prosperous future for him or herself, his/her family, his/her partner, his/her children and his/her acquaintances. of doing quite hard work of studying, mastering their craft, and contributing to the industry. Their expertise is not just a collection of theoretical knowledge but the result of real-world experience, providing concrete deliverables that has already provided added value for companies, small or large, dedication, and perseverance.

Beyond mastering concepts, these professionals have contributed to real-world solutions that have generated significant value for companies—driving revenue, optimizing operations, and enabling stakeholders, employees, and customers to benefit from their work. Their impact is not measured or has not been the concrete result of doing work from strict memorization of complex algorithms with not any tool or resource, but by their ability to solve practical challenges using the resources available in modern development environments.

Like any skilled professional, software engineers/developers/data scientist/data engineers/QA Automation testers/tech leaders that do programming, in real world companies and in real world scenarios, rely on tools such as search engines, official documentation, coding platforms, IDEs, AI-powered assistants, and collaborative repositories to enhance productivity and deliver high-quality solutions. They have already provided large and medium-scaled solutions with the help of these elements and/or were able to do so because of their critical thinking as humans, and how, based on their own human values, good faith and correct attitude, assume the challenge of doing the work or challenge, and what is that makes a Human Intelligence different from AI, and that is why companies seek to hire human software engineers/human professionals instead of relying on AI to solve a concrete problem, and that is the concrete key factor that should be strictly be evaluated from the candidate. Expecting candidates to solve problems, by memory and in complete isolation, without access to these essential resources, does not reflect real-world software development. A fair assessment should acknowledge this reality and evaluate candidates on their true ability to build, troubleshoot, and innovate—not on artificial unrealistic constraints that disregard how technology professionals actually work.

A fair assessment framework must recognize this human element, for all man y woman equally, with no discrimination by their English accent/national origin/sexual orientation/gender orientation/religion. Candidates are not just test-takers—they are professionals seeking employment to support themselves and their families. The evaluation process should respect their dignity by ensuring fairness, transparency, and objectivity.

A flawed or biased assessment can unfairly deprive a skilled professional of an opportunity, undermining years—even decades—of hard work. The psychological damage caused by such unjust treatment can be profound, leading to feelings of humiliation, frustration, and self-doubt. Beyond personal harm, these experiences can foster resentment, distrust, and a negative perception of the hiring company, its employees, and even entire industries or social groups. When candidates perceive discrimination—whether based on national origin, gender, race, or other biases—it can fuel societal divisions, increasing hostility and perpetuating systemic inequalities.

Therefore, it is crucial that technical interviews and assessments uphold ethical standards, avoid dehumanizing or subjective scrutiny based on personal feelings and impressions, and focus on evaluating true competencies without unjust practices. Companies must recognize that hiring is not just about selecting the "best" candidate but about fostering a fair, respectful, and inclusive process that values the dignity, contributions, and potential of every professional.

The importance of fairness, integrity, and recognizing a person’s contributions is emphasized in the moral and ethical teachings of the world’s major religions:

Ethical Principles from Major Religions

• Christianity ✝️
  John 7:24 – "Stop judging by mere appearances, but instead judge correctly."
  Proverbs 11:1 – "The Lord detests dishonest scales, but accurate weights find favor with him."
• Islam ☪️
  Quran 16:90 – Justice, good conduct, and forbidding oppression.
  Quran 49:13 – Righteousness is the true merit.
• Judaism ✡️
  Deuteronomy 16:20 – "Follow justice and justice alone."
• Hinduism 🕉
  Bhagavad Gita 2:47 – "You have a right to perform your duties."
• Buddhism ☸️
  Dhammapada 256 – Justice and ethical behavior define wisdom.
• Sikhism 🏵️
  Guru Granth Sahib – "True merit comes from honest contributions."
• Taoism ☯️
  Tao Te Ching – Wisdom lies in giving and applying knowledge.
• Zoroastrianism 🔥
  Yasna 34.1 – Actions for the world’s progress are true success.
• Confucianism 📜
  Analects 15:23 – Virtue matters more than reputation.
• Shinto ⛩️
  Kojiki – A righteous heart ensures harmony.'

BACK TO INDEX

1. Purpose & Objectives

• Article 1.1: This framework ensures fairness, objectivity, and transparency in technical assessments/technical interviews.
  
• Article 1.2: It eliminates biases, unfair judgments, and subjectivity by establishing clear, measurable, and justifiable criteria.
  
• Article 1.3: The document also addresses intrusive monitoring practices and unjustified disqualifications based on personal assumptions.
  

2. Core Principles of Fair Assessments

2.1 Objective Criteria

• Article 2.1.1: Assessments must be based on predefined, measurable, and skill-based criteria.
  
• Article 2.1.2: Evaluations should measure problem-solving ability, technical proficiency, and logical thinking, rather than arbitrary factors.
  
• Article 2.1.3: The focus must align with real-world development practices, recognizing that most coding is done with reference materials.
  
• Article 2.1.4: Candidates should be allowed to use tools they rely on, such as AI tools, Google, and GitHub repositories, to reflect realistic work environments.
  

2.2 Transparency

• Article 2.2.1 Advance Disclosure of Assessment Criteria: All candidates must receive the assessment criteria before the test begins. The criteria must be provided in a document of no more than one page, containing specific topics from which questions and answers can be generated for study purposes. The total number of questions should not exceed 500. This document must be sent at least one week in advance to allow candidates to prepare adequately on very specific topics.
• Article 2.2.2 Transparent Scoring and Real-Time Evaluation: The scoring methodology, including weightage for different aspects like correctness, efficiency, and maintainability, should be shared. Additionally, each question must have an assigned score, clearly specifying how much each one contributes to the final evaluation. The assessment should be graded in real-time as the candidate progresses through the test, allowing them to track their performance immediately.
• Article 2.2.3: Evaluators must provide feedback with justifications for any deductions.
• Article 2.2.4: Each question must be scored immediately after it is answered (on a scale from 1 to 10) and documented for the technical recruiter. Both the technical interviewer and the candidate must carry on this process at the time of technical assessment/interview.
• Article 2.2.5: Evaluators should also self-assess their grading to ensure objectivity.
• Article 2.2.6: Interview Report Transparency
  • 2.2.6.1. Purpose:
    To promote fairness, accountability, and transparency in technical interviews and assessments.
  • 2.2.6.2. Policy:
    After each technical interview or assessment, the evaluator must prepare a comprehensive interview report. This report must include:
    • 2.2.6.2.1. Full List of Questions Asked:A clear and complete record of all technical, behavioral, or situational questions posed to the candidate during the session.
    • 2.2.6.2.2. Candidate’s Responses:A written summary or transcript (where applicable) of the answers given by the candidate, including code snippets, system design sketches, or verbal explanations.
    • 2.2.6.2.3. Evaluation Notes (Optional for Candidate):Internal evaluator comments can be included for HR purposes but should be shared with the candidate only if phrased constructively and respectfully.
    • 2.2.6.2.4. HR Transmission:HR is responsible for sending this report to the candidate at the time of delivering feedback—regardless of the outcome (selected or not selected). This ensures candidates have full visibility into how their performance was assessed
    • 2.2.6.2.5. Rationale:This process reinforces trust in the hiring process, empowers candidates with clarity on their evaluation, and protects the company from claims of bias or unprofessionalism.
  • Article 2.2.7: Candidate Right to Use Personal AI Notes or Record Session
    • 2.2.7.1. Purpose:
      To ensure candidates have fair and equal opportunity to demonstrate their capabilities and retain access to their thought process and responses.
    • 2.2.7.2. Policy:
      • 2.2.7.2.1. Use of Personal AI Notes or Tools: Candidates are allowed to use their own AI-powered notes or personal digital assistants (e.g., note-taking apps, summarization tools, context-aware memory tools) during the interview, as long as:
        • - The tools are managed by the candidate.
        • - The tools do not involve real-time external assistance (e.g., live chat with LLMs or human tutors).
        • - Usage is disclosed at the beginning of the session if required by company policy.
      • 2.2.7.2.2. Right to Record Interview Session:Candidates may request permission to record the session (audio or video) or take comprehensive written notes for their own review. This promotes:
        • 2.2.7.2.2.1. Personal reflection and self-improvement.
        • 2.2.7.2.2.2.Transparency in the assessment process.
        • 2.2.7.2.2.3.A record in case of disputes or appeal.
    • 2.2.7.2.3. Evaluator Obligation: Evaluators must not obstruct reasonable efforts by candidates to document the session, unless restricted by company security policies (which must be declared in advance).
    • 2.2.7.2.4. Rationale: Enabling candidates to document their interview experience—through AI tools or recordings—supports transparency, self-improvement, and equal access, especially for neurodiverse individuals or non-native speakers who may benefit from reviewing complex exchanges.

2.3 Consistency

• Article 2.3.1: Every candidate should be assessed under the same conditions.
• Article 2.3.2: The same problems, scoring criteria, and time limits should apply to all applicants at the same level.

2.4 No Bias or Unjust Practices

• Article 2.4.1 Evaluators must avoid biases related to nationality, gender, background, or behavior.
  
• Article 2.4.2 AI-assisted tool usage should not be assumed without direct and verifiable evidence.
  
• Article 2.4.3 No candidate should be unfairly accused of misconduct without objective proof.

3. Assessment Design Guidelines

3.1 Defining Skill Requirements

Article 3.1.1. Clearly outline the skills being tested, such as:
Algorithms and data structures
System design
Database management
Cloud computing
Programming language proficiency

3.2 Avoiding Trick Questions

• Article 3.2.1 Assessments should reflect real-world tasks and avoid impractical questions.
  
• Article 3.2.2 Questions should allow candidates to demonstrate practical knowledge.
  
• Article 3.2.3 Theoretical questions should be disclosed in advance, as the volume of potential topics is vast and unrealistic to memorize.

3.3 Balanced Difficulty Levels

• Article 3.3.1 Provide a mix of easy, moderate, and hard questions to evaluate all skill levels fairly.
  
• Article 3.3.2 Ensure time constraints are reasonable and aligned with task complexity.
  
• Article 3.3.3 Allow the use of documentation and reasonable online resources to reflect industry practices.

4. Evaluation Criteria

4.1 Objective Scoring Rubric

• Article 4.1.1 Code correctness: 40%
  
• Article 4.1.2 Efficiency and performance: 20%
  
• Article 4.1.3 Code readability and maintainability: 20%
  
• Article 4.1.4 Problem-solving approach: 20%
  
• Article 4.2 Automated Grading for Objectivity
  
• Article 4.2.1 Utilize automated grading systems where possible to eliminate bias.
  
• Article 4.2.2 If manual grading is required, it must follow predefined rubrics with documented justifications for deductions.

4.3 Justification for Deductions

• Article 4.3.1 Evaluators must provide clear explanations for score deductions.
  
• Article 4.3.2 Vague feedback like "solution is not optimal" is insufficient; specific reasoning must be given.
  
• Article 4.3.3 Disqualifying a candidate for missing a few advanced questions while correctly answering fundamental ones is unfair.

5. Evaluator Responsibilities

• Article 5.1 Evaluators must be trained on objective assessment methodologies.
  
• Article 5.2 They must provide constructive, actionable feedback.
  
• Article 5.3 They must not alter evaluation criteria mid-assessment.
  
• Article 5.4 Personal biases should be actively avoided.
  
• Article 5.5 Each question must be scored (1-10) with documented justification.
  
• Article 5.6 Evaluators should self-assess their own grading for biases.
  
• Article 5.7 Lengthy explanations that pressure candidates should be avoided, as they create an unfair psychological burden.

6. Unjust Practices in Assessments

6.1 Forced Screen Sharing & Privacy Violations

• Article 6.1.1 Mandatory screen sharing during coding assessments violates candidate privacy.
  
• Article 6.1.2 Candidates should not be forced to use a webcam or share their entire screen unless necessary.

6.2 Responsible Use of AI in Assessments

Modern software development assessments aim to evaluate the ability to implement solutions within a given timeframe. With technological advancements, artificial intelligence (AI) has become an essential tool for optimizing this process. However, restricting its use in evaluations contradicts the reality of modern development. AI is here to stay, and prohibiting it in assessments is like trying to block out the sun with one’s hands. Instead of banning AI, assessments should focus on a candidate’s ability to use it strategically, assimilate its output, and enhance the quality of their solutions.

• Article 6.2.1: AI tools may be used as part of the development process, provided that the candidate demonstrates critical thinking and the ability to assimilate and integrate AI-generated information into a coherent solution.
• Article 6.2.2: Evaluation should focus on how well candidates refine, adapt, and improve AI-generated content rather than solely detecting AI usage. Candidates must show an understanding of the concepts behind the solutions they present.
• Article 6.2.3: If there are specific restrictions on AI tool usage, these must be clearly stated before the assessment begins. Otherwise, candidates should be encouraged to use AI responsibly as a complement to their problem-solving skills.
• Article 6.2.4: Candidates should also be assessed on their ability to maximize AI's potential to enhance deliverables beyond the initial acceptance criteria. This includes optimizing efficiency, improving quality, and innovating solutions that exceed expectations, by using AI instead of making restrictions of use of AI.
• 6.3 Real-World Development Considerations

Since modern software development involves referencing documentation, assessments must allow:

• Article 6.3.1. Reasonable use of reference materials.
  Article 6.3.2. Copying, pasting, and adapting existing code where appropriate.
  Article 6.3.3. Open-book or open-resource formats where applicable.
  Article 6.3.4. Use of tools like Chat GPT, AI hints, Google, Stack Overflow, and GitHub repositories.
  Article 6.3.5. Use of an IDE or personal tools without assuming misconduct.

7. Candidate Rights & Dispute Resolution

7.1 Right to Know Scores and Feedback

Article 7.1.1. Candidates must receive detailed feedback, including:
Final score breakdown
Justifications for deductions
Improvement suggestions

7.2 Right to Appeal

Article 7.2.1: A transparent appeal process must exist for candidates who believe they were unfairly evaluated. Article 7.2.2: Appeals should be reviewed by an independent panel, not the original evaluator. 7.2.3 If a candidate is accused of misconduct, they should have the opportunity to present their case with supporting evidence.

7.3 Right to Document the Assessment

7.3.1 Candidates should have the right to:

Article 7.3.1.1: Record the interview (audio-only) for transparency.
Article 7.3.1.2: Document all questions and answers.
Article 7.3.1.3: Provide their own self-evaluation of answers.
Article 7.3.1.4: Submit this documentation for HR review.
Article 7.3.1.5: Have their answers validated by an AI system to ensure fair grading.

8. Conclusion

Article 8.1: A fair technical assessment framework ensures evaluations are conducted transparently, objectively, and without bias.
Article 8.2: Companies must adopt standardized scoring criteria, avoid invasive monitoring practices, and allow candidates to appeal unfair judgments.
Article 8.3: By adhering to these principles, technical assessments can maintain integrity while ensuring that all candidates are evaluated fairly on their true merit.

TECNICA DESINFLE