Futuristic presentation

Harnessing AI to Forecast Employment Trends

PREDICTIVE ANALYTICS IN JOB MARKET

Critical Reasoning Bertrand Ludmilla, 2022190211

https://www.gartner.com/en/articles/9-future-of-work-trends-for-2024

introduction

• 2024, the dynamic of workplace has evolved
• Gartner a provider of research and consulting services for businessess in the IT sectors
• AI as a creator of job and opportunities.

How AI forecast changes but also guide organizations in the adapting and evolving word of employment and technology?

Foundations of AI and Predictive Analytics

• Predictive analytic
• AI machine learning and deep learning are at the heat of predictive analytic
• AI is set to enhance workforce opportunities rather than diminish them
• Identify emerging skills and competencies that will be in high demand
• shift in employee value position -> how this can impact productivity, employee satisfaction and talent retention

e.g: 4 day Worker Statistics to Change How You Think in 2024 (tech.co)

• 59% of companies are open to adopting a 4-day workweek, and a significant 93% of senior leaders in AI-centric companies are considering or have already implemented this model.

• supervised learning though function that maps input data (historical job market trends) to an ourput (future industry growth)
• unsupervised learning that uncover hidden patterns in job market data
• probabilistics reasoning which enables AI system to to make predictions about future rend by calculating probabilities

2.2 importance of understanding

application of ai in job market

2.1 MACHINE LEARNING

• analyzing job posting (required skills, qualification, experience levels)
• real-time snapshot of the job market
• predicting industry growth (economic indicators, company annoucement etc.)
• forecast industry trends

study case: linkedin

graph data base: liquid

• Graph database at LinkedIn
• Manage and processes the relationships between entities (members, company, job etc,)
• 99,99% availability for LinkedIn 930+ million members
• easy algorithm language so developers can easily build and modify features
• People You May Know: PWMK

PYMK triangle closing queries

viewing member (vm): current users logged in and viewing the suggestions First-degree Connection 'F': people the viewing member is directly connected to. Second-degree connections 'S':people not directly connected to the viewing member but are connected to the viewing members 1st degree co. 'F2' are mutual connection with someone 'S4'

study case: linkedin

Candidate Selection: LIquid, LinkedIn's graph database, initially selects a broad set of potential connections from the Economic Graph. Feature Fetching & Generation: Data attributes (features) relevant to each candidate are retrieved and generated, likely from various data sources. Candidate Filtering: Unnecessary candidates are filtered out, streamlining the selection to those most likely to be relevant. Model-Based Scoring: Candidates are ranked using machine learning models, which may utilize features stored in Venice (key-value store) and insights from Pinot (analytics platform).

PYMK achitecture Utilizing Liquid for graph traversal

study case: linkedin

limitation and potential biases

• predictive analytics models are probabilistic and carry uncertainty -> can lead to poor decision making by individuals, busines, and policymakers.
• due of the changing dynamic in job market that is influences by a multitude of factors, economic shift, technological advances and policy change -> AI must continually be updated + risk of outdated
• ethical considerations in how prediction are used +++ if its can affect people's careers and lives.
• the widespread use of AI i job market analysis can have a broader societal impacts. -> more existing inequalities and impacting the distribution of economic opportunities,

ETHICAL CONSIDERATION IN USING AI FOR JOB MARKET ANALYSIS

• Data privacy and Cosent: Ethical use of AI necessitate transparent data collection practices and respect for user consent

• Bias and Fairness: AI are only as unbiased as the data they are trained on.
• Transparency and Accountability: The 'black box' nature of some AI sytm can make it difficult to understand how decision are made
• predictive analytics can influence which job role are valuable or likely to grow, so may skewing investment in education and training.

AI and predictive analytics are revolutionizing our approach to understanding and navigating the future of the job market.

Conclusions

It can forecast employment trends, idenity emerging skills, predict industry shifts, offering organizations and individuals a 'powerful' tool to prepare for the future. However, we shall remain careful of how we use it, including its ethical concerns and its limitations.

thank you!

resources

• https://www.gartner.com/en/articles/9-future-of-work-trends-for-2024
• https://tech.co/hr-software/4-day-workweek-statistics
• https://recruitee.com/articles/analytics-in-recruitment
• https://online.hbs.edu/blog/post/predictive-analytics
• https://recruitee.com/articles/analytics-in-recruitment\
• https://economicgraph.linkedin.com/content/dam/me/economicgraph/en-us/PDF/skills-first-report-2023.pdf
• https://www.linkedin.com/blog/engineering/talent/federated-anti-abuse-defense-ecosystem-using-ai-migration
• https://economicgraph.linkedin.com/

• https://www.linkedin.com/blog/engineering/graph-systems/liquid-the-soul-of-a-new-graph-database-part-1
• another class 'info-sytem info-society' in which we discussed a lot about AI.

disucussion questions

1. Considering the current adoption rates of a 4-day workweek among AI-centric companies, what impacts might such a shift have on employee satisfaction and productivity?

1. How can society ensure that the benefits of AI-driven predictive analytics in the job market are equitably distributed?
2. In what ways might the use of AI in predicting employment trends impact worker rights and job security?
3. In terms of data handling, what ethical practices shoud be implemented to protect personal information ehrn using AI in labor makret analysis?
4. Reflecting on the case study, what lessons can be learned about the integration of AI in strategic planning within organizations?