STANDING STILL ON A TREADMILL

 Mitchelle ,

 

 

Some 60 years ago , Adlai Stevenson , a US Presidential Candidate said :

 

“ On an accelerating treadmill , you have to keep running faster and faster , in order to stand still in one place “

 

With ever accelerating AI , businesses would need to do the same – innovate faster or get toppled !

 

In my yesterday’s note , I spoke about using AI for “ MONEY MAKING “ applications ( and keep away from “ Money Saving “ apps )

 

For headhunters , this could take following forms :

 

Ø   Provide to our clients , some “ Value Added Services “, and charge extra for the same,  OR

Ø   Increase no of clients ( amount of business ) by offering such Value Added services for FREE

 

My following chat this morning with ChatGPT , lays down that path for Value Added Services

 

Hcp

 

 

 

ChatGPT :

 

The graphical profiles you're describing for the candidate's résumé present an in-depth and quantitative way of evaluating the candidate's skills, project experience, salary, tenure, and career progression. Here's a breakdown of how these profiles function:

1. Skills Profile:

• Graphs: Represent the candidate's proficiency in three key skills compared to others in the database.
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• Metrics:
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• Raw Score: Based on keywords from the résumé, this score reflects how proficient the candidate is in these skills.
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• Percentile Score: Shows where the candidate stands among peers.
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• Sigma Values:
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• -1 Sigma: Below average proficiency (bottom 16% of the population).
• +1 Sigma: Above average proficiency (top 16% of the population).
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• Mean: The average score of the candidate's peer group, with the candidate's score compared to this.
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• Insights: If a candidate's score is beyond +1 sigma, they are highly skilled; if below -1 sigma, their skills are relatively weak compared to peers.
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2. Project Exposure:

• Graph: Tracks the number of projects completed over time, along with technologies used and project duration.
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• Purpose: Highlights the candidate's hands-on experience with specific technologies and project management over their career.
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• Table: Details of individual projects, including technology, timeline, and duration.
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3. Salary Profile:

• Graph: Displays the candidate's current salary in comparison to peers.
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• Sigma Values: Same structure as the skills profile, indicating whether the candidate is overpaid (beyond +1 sigma) or underpaid (below -1 sigma).
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• Table: Shows the number of candidates earning less, equal to, or more than the candidate, alongside the total population size.
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4. Tenure Profile:

• Graph: Represents the length of time the candidate has spent in various roles compared to peers.
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• Purpose: Helps assess job stability. A shorter tenure might indicate frequent job changes (potential "job jumper"), while a longer tenure suggests more stability (a "contented cow").
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• Sigma Values: Measure how stable or mobile the candidate is in their job history relative to others.
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• Table: Breakdown of the candidate’s tenure vs. the population.
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5. Career Profile:

• Graph: Tracks the candidate's career progression through different roles and designations.
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• Purpose: Provides insight into how quickly the candidate has advanced in their career. A slow progression may indicate stagnation, while a faster trajectory suggests ambition and success.
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• Table: Detailed career timeline, including:
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• Age at various milestones (first degree, second degree, first job).
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• Years of experience.
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• Potential discrepancies (e.g., mismatched rows) that might indicate inconsistencies in the résumé.
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Conclusion:

The detailed use of statistical analysis in these profiles allows recruiters to make data-driven comparisons between candidates, ensuring a more objective selection process. The sigma values, percentile ranks, and graphical representations highlight strengths, weaknesses, and trends in a candidate's career, skills, and compensation. This method enhances transparency and provides a thorough understanding of the candidate in comparison to a larger professional pool.

 

I  said:

Hey , thanks for your analysis of these Graphical Profiles .

 

Now , is there any way / method whereby , I can super-impose onto each separate graph ( of a given candidate ) , answers to the following questions that arise in the minds of an Interviewer as she looks at each graph ?

 

If you are able to " superimpose " such interpretations on graphs , it would hugely improve the QUALITY of an interviewer's understanding of a candidate .

 

Here are some of these questions :

 

> Profile Questions

 

Career History

 

What is his total experience?

 

Does this match with the experience required for our position/vacancy?

 

During this “total experience”, how many jobs has he changed ?

 

Does he seem to be a “job-jumper” ?

 

Does he seem to have stuck to the same organization for a long time?

 

What could be the reason?

 

Does he seem to have stuck at the same “Designation – Level “ for a long time ?

 

Was he “un-promotable” ?

 

He seems to have got very frequent promotions /salary-raise.

 

Does he come through as a “Fast Track” candidate?

 

Given his “Average/Longest/Minimum” tenure with the companies where he worked, how long can we expect him to stay with us ?

 

He is in his current job for less than a year. Why is he keen to quit within such a short period?

 

Is he platueing? i.e. rose very rapidly in early years but now seems to have slowed down.

 

In terms of the size/turnover/reputation of the companies where he has worked, does he seem to have steadily “progressed”?

 

Are there any reversals? i.e. leaving a large/organized company to join a small/family owned company.

 

Has he spent his entire career in one city or is he mobile?

 

Does his “Total Years of Experience” tally with his current age ?

 

If not, does he seem to have “inflated” his experience ?

 

Or, he should have got 20 years of experience by now, but graph shows only 12 years ! Has he failed to “list” some “unfavorable” tenures? e.g.: where his services were terminated or where the service was “temporary/contractual” ?

 

Did any of the companies that he worked for, belong to his father/relative?

 

If so, what was his designation/salary during such tenure?

 

Was that commensurate with his qualifications/experience?

 

During his career, were there any periods of “self-employment” ?

 

If so, has he listed those in his “Career History” section?

 

What factors made him “start/give-up” such self employment?

 

Salary

 

He seems to have picked-up a huge salary-jump, everytime he changed jobs.

 

What kind of salary-increase would he expect us to offer to motivate him to change ?

 

Can we afford ? Would that upset a lot of our existing employees and demotivate them ?

 

Would we be upsetting the apple-cart ?

 

 

Has his salary kept pace with his rise up the career ladder ?

 

Is his salary lagging behind/ way ahead of a majority of his co-professionals ?

 

How does it compare with similar professionals in our organization ?

 

What seems to be his “trade-off” between a) Salary b) Designation Level ?

 

Would he be prepared to accept . Same salary ( his current salary ), if offered a higher/better designation ? .

 

Same designation ( his current designation ), if offered a higher salary ?

 

 

Academic Inputs

 

At what age did he pass . SSC . HSC . Bachelor’s Degree . Master’s Degree

 

Do these seem reasonable ?

 

What no. of years did he take to pass . Bachelor’s Degree . Master’s Degree

 

Does he seem to have taken longer than stipulated period?

 

Could he have failed once – or more?

 

Were any of these qualifications obtained through “Correspondence – Course” ?

 

or through “Part-time study” in evenings ?

 

If so, for how long ?

 

What about the educational institutions/universities that he attended?

 

Are these recognized/reputed/accredited institutions?

 

Why did he not pursue higher studies in the same institution/university ?

 

Why did he have to go to another state/country ?

 

Is there an “overlap” between . Completion of his education, and . Start of his first job ?

 

If yes, how did he manage that ?

 

If there is a substantial “gap” between . Completion of his education and . Start of his first job Then, what did he do during that period ?

 

He seems to have obtained more than one degree/diploma during the same year. How did he manage that ?

 

 

ChatGPT said:

To improve the quality of a candidate's evaluation, you can integrate the interviewer’s questions directly into the graphical profiles. Here's a methodical approach to "superimpose" answers onto each graph:

 

1. Overlay Relevant Data:

For each graph (Career History, Salary, Academic Inputs), you can add visual annotations or interpretive text boxes directly onto the graph that highlight the candidate’s standing with respect to key questions. Here’s how:

• Career History:
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• Total Experience: Annotate the total years of experience directly on the graph. If it matches the position's requirements, indicate this with a checkmark or label.
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• Job Changes/Jumping: Show a trendline highlighting how many jobs the candidate has changed over time. You can add a note or color-coding for frequent job changes or long stints at the same company.
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• Designation Stagnation: Use color or markers to indicate periods where the candidate has been in the same designation level for a long time.
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• Promotion Frequency: Add lines or markers that highlight promotion events. If the frequency of promotions is high, add a "fast track" note.
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• Job Stability Prediction: Based on past tenure lengths, you can annotate an expected tenure with a small chart or icon that predicts the likelihood of the candidate staying long-term.
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• Salary Profile:
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• Salary Jumps with Job Changes: Add arrows or markers to indicate salary jumps after each job change. Use a comparative marker to indicate the average salary increase with job changes, and compare it to the industry average or internal benchmarks.
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• Salary vs. Designation: Annotate where the salary aligns or deviates from career progression. Highlight any significant deviations using a color scale to indicate salary underperformance or over-performance.
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• Salary Expectations: You could introduce a salary expectation bar based on their past raises. Show visually how a new offer could compare.
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• Academic Inputs:
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• Degree Timelines: Add a timeline for when the candidate completed each degree and highlight periods that are longer than normal or gaps between education and career start. Use markers for possible reasons (e.g., part-time study, work during study, etc.).
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• Educational Gaps: Mark any gaps between degree completion and job start with a question, e.g., "What was done during this period?"
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• Overlapping Studies: If there’s overlap between two degrees, annotate it with an inquiry to suggest further investigation by the interviewer.
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2. Graphical Enhancements:

• Color Coding: Use colors to indicate answers to the interview questions. For instance:
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• Green for positive trends (e.g., fast promotions, high salary relative to peers).
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• Red for negative indicators (e.g., frequent job changes, low tenure, unexplained gaps).
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• Symbols: Use symbols (e.g., arrows, circles, question marks) to highlight anomalies like sudden salary jumps, extended study periods, or potential "job-jumping" tendencies.
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3. Dynamic Interpretation Layer:

• Create a pop-up or hover-over feature (in digital formats) where, when the interviewer hovers over a point on the graph, a contextual interpretation appears (e.g., "Why did the candidate leave this position after just six months?" or "This rapid salary increase might indicate job-hopping").
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4. Cross-Graph Integration:

• You can also cross-reference insights from different graphs. For example:
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• Link salary growth to promotion frequency or tenure stability.
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• Compare career timeline (e.g., degree completion) with the start of professional experience to check for discrepancies or overlaps.
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By blending this method of visual and textual annotation into each graph, an interviewer can gain a more comprehensive and intuitive understanding of the candidate’s profile, reducing the effort required to manually interpret these questions.

 

Would you be interested in tools or software recommendations to implement these changes?

 

I said:

Hey , ChatGPT , if you could recommend software with which , I can ( almost ? ) get the text observations superimposed AND , in next stage, get these textual remarks / annotations to SPEAK UP ( TTS ), that would be great !

 

Chatbots have been around for a long time . Now I want your recommendation to turn a RESUME GRAPHICAL PROFILE into a " Special Purpose " AI Agent ( call it INTERVIEW BOT ? )

 

ChatGPT said:

Turning résumé graphical profiles into a "Special Purpose AI Agent" (an Interview Bot) is an innovative idea! It combines data visualization, text annotation, and text-to-speech (TTS) capabilities. Here's a roadmap of tools and software that can help you achieve this, stage by stage:

 

1.       Stage 1: Superimpose Textual Observations on Graphical Profiles

 

To annotate the graphical résumé profiles with textual observations dynamically, you can use the following tools:

• Microsoft Power BI:
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• Why: Power BI allows for advanced data visualizations and annotation capabilities. You can create dynamic reports and dashboards that include annotations, and with custom visuals, you can layer textual insights over the graph.
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• How: You can programmatically link specific insights to visual elements. Power BI integrates well with data sources like résumés and allows for cross-filtering between various résumé graphs (e.g., salary vs. tenure).
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• Bonus: Power BI offers interactive dashboards that an interviewer can manipulate to focus on specific insights.
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• Tableau:
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• Why: Tableau’s advanced visualization features allow you to overlay text annotations, use interactive pop-ups, and even introduce dynamic filters that help interviewers explore various "what-if" scenarios.
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• How: You can add annotations or dynamically update them based on user interaction. Tableau has great functionality for showing trends and outliers, which can help surface key observations.
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• Bonus: Tableau's ease of integration with data allows for a smooth import of résumé data and the creation of customizable résumé views.
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•  
• Google Data Studio:
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• Why: It’s a free, accessible alternative to Power BI and Tableau, with strong customization features for creating interactive reports with annotated graphs.
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• How: You can add text boxes, visual cues, and pop-ups that explain key points from a graph or résumé profile.
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• Bonus: Easily shareable across different platforms and user-friendly for interviewers who need quick insights.
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•  

2. Stage 2: Adding Text-to-Speech (TTS) for Interactive Feedback

Once the graphical annotations are in place, the next step is enabling TTS so that the bot can “speak up” the insights. For this, you can integrate the following tools:

• Microsoft Azure Cognitive Services (Text-to-Speech):
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• Why: Azure’s TTS service is highly customizable and can convert textual insights into human-like speech. You can integrate TTS with your graphs from Power BI or a custom interface.
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• How: The TTS feature can be triggered by user interaction (e.g., when hovering over or clicking a graph element, the bot reads out the relevant interpretation).
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• Bonus: Azure TTS also offers natural-sounding voices and can be integrated with chatbot frameworks if you want to scale into full conversational AI later on.
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• Google Cloud Text-to-Speech:
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• Why: Google Cloud offers state-of-the-art TTS that can be embedded into web applications. It supports various languages and offers natural-sounding voice models.
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• How: You can integrate this with an annotated graph or dashboard built in Google Data Studio or a custom app.
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• Bonus: It’s highly customizable in terms of voice speed, pitch, and language, allowing for a tailored experience.
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• IBM Watson Text-to-Speech:
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• Why: IBM Watson provides an easy way to convert textual content into speech in real-time. It offers high-quality, natural-sounding voices and can be easily integrated with chatbots or standalone applications.
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• How: Use Watson TTS to convert the annotations into spoken insights. You can link specific TTS responses to each graphical profile.
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• Bonus: Watson has strong support for AI-based workflows, which can lead to a more advanced AI-driven Interview Bot.
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3. Stage 3: Combining into a "Special Purpose AI Agent" (INTERVIEW BOT)

 

To create a fully functional Interview Bot that reads and interprets résumé data, you can integrate the above technologies into a chatbot or voicebot framework. Here are options for building this advanced tool:

• Rasa (Open Source Conversational AI):
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• Why: Rasa allows you to create custom conversational AI systems. You can integrate data from your résumé graphs, let the bot answer interview questions, and have it "speak" through TTS services.
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• How: Use Rasa’s custom actions and NLU (Natural Language Understanding) to design conversational flows that take résumé data and trigger TTS responses for specific questions.
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• Bonus: It’s flexible and customizable, allowing you to fine-tune how the bot responds to specific interview-related prompts.
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• Dialogflow (Google):
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• Why: Google Dialogflow is a natural choice if you’re using Google Cloud TTS or Data Studio. It can handle voice and text-based interactions.
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• How: Connect Dialogflow to your annotated résumé data so it can respond with relevant insights based on interviewer queries. You can also trigger the bot to "speak" its answers using Google’s TTS.
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• Bonus: It integrates easily with other Google services and scales well for large implementations.
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• Microsoft Bot Framework:
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• Why: Microsoft Bot Framework works seamlessly with Azure services, including Power BI and Azure TTS.
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• How: You can design a bot that pulls data from Power BI, interprets it, and converts those insights into spoken or text-based responses.
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• Bonus: The framework integrates natively with Microsoft Teams, web, and mobile apps, allowing for flexible use across platforms.
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4. User Experience (UX) Consideration:

 

To ensure the Interview Bot is effective:

 

• Interactive UI: Use an interactive dashboard (e.g., built in Tableau/Power BI) where users can hover, click, or ask questions that trigger TTS responses.
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• Seamless Data Integration: Ensure that the graphical data and résumé insights are updated in real-time, keeping the bot’s responses accurate and relevant.
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• Voice Customization: Offer a selection of voice types and speech patterns to make the Interview Bot sound engaging and human-like.
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5. Advanced AI Features (Future Expansion):

 

Once you have the bot functioning, you can explore:

 

• Natural Language Processing (NLP): To allow interviewers to ask follow-up questions and have the bot analyze deeper insights from the résumé.
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• Machine Learning for Predictions: Predict candidate behavior based on patterns in career history, salary, and tenure graphs (e.g., likelihood of job-hopping).
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• Emotion Detection in Speech: Add layers of emotional intelligence to detect the candidate’s potential response to a job offer (through their historical data).
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By blending graphical visualization tools (Power BI, Tableau) with TTS services (Azure, Google, Watson) and AI frameworks (Rasa, Dialogflow, Microsoft Bot Framework), you can create a powerful Interview Bot that makes candidate evaluation more dynamic, engaging, and insightful.

 

I said:

Hey , many thanks for your valuable advice.

 

With your suggestions , it may not take too long to implement , CANDIDATE BOT and RECRUITER BOT , as envisaged in my following blog > https://myblogepage.blogspot.com/2021/11/a-wishful-thinking-no-already-here.html // where I wrote :

 

" How long before these BOTS beat humans at “ negotiating to win “ ? And beat other BOTS or other humans , who too are negotiating with the same

 

human for a favourable outcome ? As in case of dozens of jobseekers being interviewed by a recruiter

 

 

Not inconceivable if someone comes up with a

 

CANDIDATE BOT ( a software agent of the jobseekers ) , which gets pitted against a RECRUITER BOT ( a software agent of the employers ) " /

 

/ What do you think ?

 

ChatGPT said:

 

You're absolutely right—

 

it seems quite feasible to develop CANDIDATE BOT and RECRUITER BOT, given the current advancements in AI and negotiation algorithms.

 

Both bots could leverage AI-driven decision-making processes to evaluate job offers, counteroffers, and employment terms, optimizing for the goals of the jobseeker and the employer.

 

Key Capabilities for CANDIDATE BOT and RECRUITER BOT:

 

1.               Profile Analysis & Data Extraction:

 

o    CANDIDATE BOT can analyze a candidate’s résumé, skills, and experience to prepare responses, salary expectations, and preferences (e.g., work-life balance, benefits). This could include leveraging résumé graphs similar to the one you’re working on.

o     

o    RECRUITER BOT could analyze a job posting, company requirements, and historical hiring data to craft tailored job offers based on what will likely appeal to the candidate.

o     

2.               AI-Based Negotiation:

 

o    Using negotiation frameworks (like Reinforcement Learning), both bots could dynamically adjust offers and counteroffers based on feedback and constraints from the user or their agent (candidate/employer).

o     

o    Natural Language Processing (NLP) and Sentiment Analysis could assess the tone and sentiment during the negotiation to tailor responses accordingly, whether the negotiation is happening with another bot or a human.

o     

3.               Data-Driven Insights:

 

o    CANDIDATE BOT could analyze market trends to suggest competitive salary ranges, promotion potential, or career growth paths.

 

4.               RECRUITER BOT could evaluate candidate profiles against current organizational needs and budget constraints, adjusting offers in real-time.

 

5.               Decision Optimization:

 

6.                

o    CANDIDATE BOT would optimize for the candidate’s goals (e.g., salary, work-life balance, job role).

o     

o    RECRUITER BOT would optimize for company goals (e.g., filling the role quickly, staying within budget).

o     

Potential Outcomes:

 

• Speed and Efficiency: These bots could drastically reduce the time it takes for job negotiations by quickly adjusting offers and counteroffers.
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• Objective Decision-Making: Bots would remove emotional bias from negotiations, focusing on data and optimal outcomes.
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• Multiple Candidates/Jobs: In mass recruitment, these bots could handle multiple negotiations simultaneously, improving scalability and fairness in the hiring process
•  

Given your progress on graphical résumé analysis and interview profiling, this idea of bots negotiating could become a highly efficient next step, leading to even more sophisticated talent acquisition processes.

This vision aligns well with your wishful thinking blog post! The future of recruitment might very well feature bots out-negotiating both humans and other bots.