Whom to choose?

Statement

In large organizations, the process of hiring often resembles a complex maze. Imagine you're an applicant sending in your resume. It doesn't land on a hiring manager's desk right away. Instead, it faces its first hurdle – the Applicant Tracking System (ATS). This digital gatekeeper is designed to help manage the flood of applications by scanning resumes for specific keywords.

Now, the catch with ATS is that it sometimes operates like an overenthusiastic keyword hunter. It zooms in on certain words but might miss the broader context of your skills and experiences. It's like looking at a painting and only focusing on a single color – you miss the richness and depth.

Think about it from the applicant's perspective. You're not just a list of keywords; you're a person with a unique blend of skills, experiences, and potential. The challenge here is for participants to put themselves in the shoes of applicants, to feel the frustration of being reduced to a checklist of words.

But the complexities don't stop there. Bias sneaks into the hiring process like an uninvited guest. ATS and even some ML models, if not trained meticulously, can perpetuate biases that exist in historical hiring data. It's like inheriting the flaws of the past that mainly arrived due to the inherit nature of human bias in previous hires.

So, the task at hand is not just about fixing a glitch in the system; it's about revolutionizing the hiring process.

The Challenges

1. How can we shift the focus from rigid checklists to a holistic understanding of an applicant's potential?

2. What creative strategies can be employed to encourage hiring managers and automated systems alike to appreciate the richness of each applicant beyond a predetermined set of criteria?

3. How can we break the chain of bias inherited from historical hiring data in automated systems?

4. How can we ensure that an ATS doesn't just play hide-and-seek with keywords but understands the story behind them?

Objectives

Learning from the pitfalls of the past is crucial. Take Amazon, for instance. When they tried to automate their hiring process using previous data, it led to a biased system that preferred male candidates. This tells us a vital lesson – blindly relying on historical data is like driving forward while only looking in the rearview mirror; you're bound to miss what's ahead.

So, our objective is clear: we're not here to repeat Amazon's missteps (you can read more here). We're seeking solutions that go beyond the shackles of history. We want participants to craft a system that doesn't just look at what's written on a resume but considers the person behind those words.

Expectations: Building a Holistic Hiring Ecosystem

Personality Matters:
- Develop a system that goes beyond keywords and considers the personality traits of applicants. How can technology be harnessed to gauge the human aspects that make someone a good fit for a role?
Context is Key:
- Create a solution that understands the context of an applicant's experiences. It's not just about where they've been; it's about how those experiences shaped them. How can the system delve into the context and extract meaningful insights?
Role-Applicant Alignment:
- Imagine a system that doesn't just match keywords but aligns the applicant with the essence of the role. How can technology be leveraged to ensure a more meaningful connection between the applicant and the job they are pursuing?
Technology Agnostic Solutions:
- There are no restrictions on the tools you can use. Whether it's Machine Learning, Natural Language Processing, or any other technology, the goal is to create a solution that is effective and adaptable.

Our expectation is crystal clear – we're on a quest for a hiring system that sees beyond the surface, appreciates the uniqueness of each applicant, and creates a bridge between technology and humanity. It's not just about finding the right person for the job; it's about finding the right match that goes beyond what's written on a resume.

Judgement criteria

Ideation (30%)

Unveiling Perspectives:

How well does the solution demonstrate a deep understanding of the challenges posed in the problem statement?
Does the ideation phase showcase a variety of perspectives, indicating a comprehensive examination of the problem?

Creative Exploration:

To what extent does the solution venture into innovative territories?
How creatively does the ideation phase approach the problem, introducing novel concepts and perspectives?

Human-Centric Approach:

Does the ideation phase reflect an emphasis on understanding the human side of hiring, empathizing with both applicants and hiring teams?
To what extent does the proposed solution align with the goal of reshaping the hiring process to be more inclusive and human-centric?

Innovation (30%)

Out-of-the-Box Solutions:

How inventive are the proposed mechanisms in addressing the challenges posed by ATS and bias in hiring?
To what degree does the innovation phase introduce solutions that go beyond conventional approaches?

Adaptability:

How well does the solution adapt to the evolving landscape of hiring practices?
Is the innovation phase forward-thinking, considering potential shifts in the industry and technology?

User-Centred Design:

Does the proposed innovation prioritise the user experience for both applicants and hiring teams?
To what extent does the innovation phase demonstrate a commitment to user-centred design principles?

Implementation (40%)

Feasibility:

How practical is the proposed solution for real-world application?
To what extent does the implementation phase consider the feasibility of integration into existing hiring processes?

Impact Assessment:

What potential impact does the solution have on mitigating biases in hiring and improving the overall hiring experience?
Does the implementation phase provide a clear pathway for measurable positive outcomes?

Iteration and Improvement:

How well does the solution account for feedback and allow for iterative improvements?
Does the implementation phase showcase a commitment to continuous refinement and enhancement?