Today, we’ll discuss about how to combine the powers of Riminder, the 1st AI powered Infrastructure for your talent pools that helps centralize, score and control your talent pools, and Trello to kick off automation for your hiring process.

A quick look at Trello

Trello is a project management and collaboration tool that organizes your projects into boards.
With Trello, you can track the progress of a project as the tasks move to different stages. It can help you structure almost every process in your organisation, ranging from complex IT projects to your personal roadmaps.
An increasing number of startups around the world and especially in Silicon Valley have been using it as a free ATS (Application Tracking System).
As a recruiter, it can help you set up your recruiting pipeline with columns that correspond to the different stages of your hiring process.

→ Learn more about how Trello can help your HR teams: https://blog.trello.com/trello-boards-for-hr-teams

Why Riminder + Trello?

Our Trello integration helps recruiters to kick off automation within their applicants’ workflow. It allows them to set up an efficient data pipeline that automatically feeds their Trello board with shortlists of candidates and relevant information to drive their interviews.
Therefore, they can spend more time on tasks that are more emotionally intelligent, leaving behind them the less valuable and time-consuming tasks.

How does it work?

• Step 1: Sourcing
  Riminder collects profiles (all resumes formats, all types of jobs) across all sources of candidates, whether they come from emails, folders, ATS, or any other internal and external sourcing channels recruiters may use.
• Step 2: Filtering
  The recruiter can set AI-powered filters associated to different positions to score candidates and rank them by relevance.
• Step 3: Shortlisting
  The recruiter can review candidates by giving them a “YES”, “NO” or “LATER” label or set automatic rules to send the most relevant ones to relevant workflows or third party apps.
• Step 4: Feeding
  A Trello card is automatically created for every candidate that got a “YES” action (positive review) from the recruiter. The same information can be sent to any ATS used by recruiters.
• Step 5: Interviewing
  Once the card is added to Trello, they can easily keep up with their hiring workflow. Adding comments or notes about the candidate, assigning and tagging other teammates, attaching files or documents … help to keep everyone organized and access the relevant details in one place. As the candidate moves forward in the interview process, they’ll also be able to move the card to the next list.

💡 Tips: at Riminder, we’ve created various lists as per our hiring process, i.e. Contacted, Replied, Phone screen… until Waiting for offer and the Team list for the luckiest! The integration with Trello automatically create a board with labels for different departments inspired from the job filters

How to leverage Riminder+Trello to drive your interviews?

On average, less than 20% of candidates interviewed actually get a job offer (at least in startups and SMEs). Low conversion rates can be symptomatic of a lack of clear guidelines for skills check; especially for the 1st interviews, often more focused on the cultural fit.

With Riminder+Trello, recruiters are better prepared, as they can see at a glance what skills to focus on - hard skills, soft skills and languages being automatically generated as checklists on the Trello card based on our market analysis for the job.

There are less back and forth between recruiters and managers, and the former are better equipped to challenge the latter. Not only can Riminder+Trello help raise conversion rates, but it can also increase the overall performance of the HR team.

A quick zoom on a Riminder+Trello card

When you shortlist a candidate clicking on “YES”, a Trello card is automatically created, so that none slips through the cracks. It contains all the relevant information required to continue the hiring process:

• Job Title
• Name, email and phone number of the candidate
• Candidate’s score on Riminder
• Link to Riminder’s profile and full resume
• Hard and Soft Skills and the ones automatically checked by Riminder
• Language proficiency

How to set up the Riminder+Trello Integration?

The Trello integration is really simple and only takes a few minutes directly from your Riminder platform. If you don’t have one yet, get started now!

To integrate with Trello, you should have a team created on Trello before. Here is how to create one.

And here is how to integrate your Riminder platform with Trello.

Riminder+Trello helps you keep your workflow transparent and your recruitment pipeline up-to-date. All in all, you are able to build and automate a clear and easy-to-follow workflow suiting your needs and boosting your productivity.

Process automation for recruiters (episode 1): How to automate your applicants’ workflow? was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

1 single integration = 300+ integrations with the most powerful apps to streamline your recruiting processes.
Sounds too hard to believe? But it was made possible through the latest product update Riminder + Zapier.

What is Riminder?

Riminder is the most advanced AI-powered recruiting solution to help you parse, enrich, score, reveal talent pools across channels and at scale.

A quick intro to Zapier

Zapier is the only automation tool that connects apps effortlessly, keeping data consistent across multiple apps. It basically works as a translator between web APIs. With Zapier, you can integrate +1000 apps to automate tasks and workflows without any coding or development experience.

Building an ecosystem of partners

Connect all your sources in one place

Enable synchronization with any of your sources, i.e. talent pools and recruitment channels.
Using cloud storage to keep resumes? A livechat to connect with and capture potential candidates? An online form to engage applicants?
Anywhere is fine. Riminder + Zapier allows you to import new profiles coming in from any tools, straight to Riminder.

Waste no time centralizing the resumes you collected and stop letting candidates falling through the net.

Integrate your favorite destinations

Send your shortlisted profiles to any tools relevant for the continuation of your process.
Whether it is your ATS to move candidates along the next steps of your hiring process, a CRM to proactively manage relationships with applicants, or a messaging app to send automatic answers to candidates.

For your recruiting workflow, this would imply more productive hours and candidates that are more engaged and connected.

Key benefits

• Any integration (+300 relevant apps to connect with Riminder)
• No technical knowledge needed (create Zaps in less than 5 minutes )
• Automate processes (stop doing manual work and focus on what matters)

How does it work?

Find the best integrations for you from +300 destinations and +400 sources to manage profiles.

What’s next for you?

• Join our beta program to create your first Zaps

More?

• Request a demo or get started now to start hiring more, better, faster here
• Check out our use cases
• Discover our latest updates

Introducing Riminder + Zapier to kick start Recruiting Workflow Automation was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

Endless application forms are the nightmare of every job seeker, and we all have been through this.

The internet has made it easier for people to look for jobs and companies as they would for any other important purchasing decision.

And like any other customers, they expect immediacy, customization, and smooth user experience. Something companies often tend to overlook although it is key for the success of their hiring process.

Why good UX matters? Numbers speak for themselves.

1) You only have a small window to capture them ⏱️

73% of your prospective candidates spend on average less than 30 seconds on your career website, while only 3% complete an application.

2) Your careers pages are far from being the most visited 👨‍💻

60% of candidates go up to the career webpage, while 42% stop at your landing page. The “About us” page comes 2nd in the list of most the viewed pages by candidates: candidates want to learn about your company before applying.

3) Your candidate experience matters✨

69% of candidates wouldn’t accept a job in a company for which they have a bad perception, even if they were unemployed.

Who are the valuable talents? Let’s unveil some false dogma that has been peddled as wisdom in HR for too long.

The candidates distribution breakdown comes as following:

> The Millennials 👩‍🎓
They are very sensitive to the user experience. Being born with a smartphone in their hand and fed with the UX of Silicon Valley big companies, they have a high level of expectation and demand the same experience from a careers website.

95% of Millennials candidates will spend less than 1 minute and 30 seconds on a career website.

> The Seniors 👨
They are not willing to apply to job offers because they are skeptical about the attention that will be given to their application. Most of them rather rely on their networks to land their dream job, unless it costs them nothing to apply.

98% of Seniors do not believe in job offers to find a new job.

> Talents in general 🤓
In the battle for top talent, traditional thinking often holds that putting more spokes in candidates’ wheels would attract only the most determined ones. The truth is that, the more qualified prospective candidates are, the laziest they feel to apply on your website. In fact, best talents are chased all day long by headhunters and do not feel the need to embark on a painful application process. Their tolerance for going through dozens of steps is much lower than what most companies think.

Putting more barriers in your application process favors ending up with the most actively looking job seekers, not necessarily the most qualified ones

Good news: Riminder’s got your back!

The brand new source of candidates you need is here.
We call it CVbot and it has an an immediate R.O.I.:

1HIRE MORE
It will help you to capture more than 60% of the 70% of candidates visiting your website but not dropping a resume, by dramatically disrupting the candidate experience.

2HIRE BETTER
It will not only increase the quantity of applications but also their quality, requiring less than 5 seconds to apply.

3 HIRE NOW, FASTER, ANYWHERE
The best part? Setting upCVbot is as simple as copying/pasting a piece of text on any of your web page. Easy, right?

Already proven

Even the self-driving car company http://drive.ai/ backed by the AI leader Andrew NG is using it to increase its valuable talent pool. This is how it looks like on their website.

Request a demo now! Or get started now and create a CVbot Source in less than 2 min.

Follow us on Twitter @riminderdotnet

If you want to work on AI + FAIRNESS, check our jobs page!

Why you should treat candidates like customers was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

Introducing Fairness by design

From the very beginning, our mission at Riminder has always been preventing recruiting bias to reveal everyone’s full potential. To achieve that goal we believe that building inclusive machine learning is the key.

Programming Vs. Machine Learning

As above, to get computers to do something new we used to program them.
Programming required laying out detailing every single step and action in order to achieve our new goals.

Now, even if you don’t know how to describe a certain problem, you can teach a computer how to solve it through Machine Learning.

Machine learning algorithms provide a computer with the ability to automatically learn and improve itself from experience only by drawing examples from the data. The more data and computation power they can access, the better they get.

Machine Learning Vs. Deep Learning

Deep Learning is an area of Machine Learning based on networks of simulated neurons inspired by how the human brain works. As a result, Deep Learning algorithms have no theoretical limitations and allows computers to outperform traditional Machine Learning algorithms and even the human performance in specific tasks.
In 2006 Deep learning techniques started outperforming traditional Machine learning techniques — first in Speech and Image, then Natural Langage Processing, for two primary reasons:

• Better benefit from the large amounts of data than ML techniques
• Faster machines and multicore CPU/GPU (NVIDIA) allowed both the usage of DL algorithms in production but also the emergence of new models and ideas through iterations.

Most importantly Deep Learning algorithms allow better leverage of unstructured data by engineering their features and representations to achieve a given goal without requiring any prerequisites about the data field from the Developers who are training them.

An impressive example was Kaggle competition won by a team of Geoffrey Hinton about automatic drug discovery — beating all the international academic community with no background in chemistry, biology or life sciences, all in just two weeks!

The trouble with Learning Systems

Learning systems are rapidly expanding into many areas of everyday life from healthcare to education. Amongst the excitement about these large-scale data systems, two major concerns are arising:

> A conscious one: Adversarial Examples.
> An unconscious one: Bias.

Adversarial Examples, the conscious trouble

“Adversarial examples are inputs to machine learning models intentionally designed by an attacker to cause an incorrect output prediction from the model. They are to machines what “optical illusions” are to human.

For instance by adding a small and well-calculated noise/perturbation to any image you can completely change the original prediction of a model with a high confidence.

Even worse a malicious hacker can leverage these weaknesses to fool a Learning Model and take control of your future self-driving car making it blind to pedestrians’ presence on roads.

Recent research has shown adversarial examples can still fool a system even after they have been printed.

A recent paper shows that humans are not different from computer systems and that real life adversarial examples can fool humans too.

You can read more about adversarial examples here: https://blog.openai.com/adversarial-example-research/

Bias, the unconscious trouble

The definition of the word “Bias” has been changing over the last centuries :

>14th century: Geometry diagonal line
>19th century: Law, undue prejudice
>20th century: Statistics, systematic difference between a sample and a population.

Bias in machine learning

Machine Learning systems are keeping on being integrated into the life of millions of people every day. Thus, the research on Machine Learning Bias is wholly justified.

In machine learning, the bias is commonly visualized :

• trough “under-fitting” which corresponds to a situation with “high bias & low variance”
• in contrast with “over-fitting”, a situation of “low bias & high variance”.

Bias beyond machine learning

Considering the Bias problem in Machine Learning as a purely technical problem leaves out a whole part of the picture.

Training data is often biased by the world large history of human undue prejudices.As a consequence, this introduces a new intrinsic bias (legal) in the learning systems that cannot be solved even with model validation techniques. As consequence, Machine Learning models can still be biased from a legal perspective while being unbiased from a technical standpoint.

Recruitment, a practical use-case

Even with the best intentions, it’s impossible to separate humans from their own biases.

In recruiting most of biases occur and are perpetrated mainly because of the use of Keyword search Engines to screen candidates. Indeed, since these keyword systems are hard to tune, most of the people end up typing well-known schools and well-known companies which favours prestige over the real potential of the candidate. Furthermore, because these tools are not smart enough to translate a diverse set of experiences and backgrounds they :

> overlook the majority of high potential candidates
> reveal false positives.

Revealing people full potential

At Riminder, we believe that the main challenges that recruiters are facing today and will be facing in the next decades are:

• handling the increasing diversity of talents ( mainly with talent globalization and the career path diversity )
• keeping up with the rapidly changing job landscape ( +60% of the jobs that we need in the next 20 years do not exist yet, source: McKinsey).

By eliminating bias, in recruitment we allow businesses to identify 3x times more relevant talent within their existing databases while cutting 1/2 the time to fill a position.

By qualifying (parsing+enriching+scoring) every element of the career path of the candidate (namely the working experiences, projects, educations, hard skills, soft skills, transitions, etc.) we identify the overlooked connections between positions.
At the opposite of a keyword system, for which a “sous-chef” and an “ event manager” are unrelated, our technology understands the hidden correlations between them — such as being highly organized and being able to perform well under pressure.

Riminder.net AI Technology

Our technology is based on 4 layers of AI:

• First Parsing: we use Deep Computer Vision and Natural Language Processing to parse each resume as a structured data profile.
  Learn more: https://medium.riminder.net/hr-software-companies-why-structuring-your-data-is-crucial-for-your-business-f749ecf3255a
• Second Enrichment+ Scoring: on the top of this structured data, we built a general layer based on the millions of career paths we analyzed
• Third Custom Scoring: a custom layer based on the internal data of the company and feedbacks from the recruiters to fit its specific needs regarding culture and requirements.
• Fourth Revealing: explaining the evidence behind every recommendation taken by our algorithms.

Quick tips for candidates that are applying to companies that are not using Riminder:

>Language bias: always send a resume in the most commonly used language in the company. People focus on the language that is the easiest for them.
→Interview likelihood: +6.8%.

>Layout bias: always send a one-pager mono-column double-color resume. People focus on the layouts that is the easiest to read.
→Interview likelihood: +N columns= -3.2% x N and +N pages = -3.8% x N.

Formal Notions of Fairness

Fairness of Allocation

Fairness can be measured using a variety of metrics, among which are:

Statistical Parity: belonging to a subgroup of a large community should not change the outcome of a classifier.
Individual Fairness: Ensures that if two individuals are similar according to metric the outcome of the prediction should follow that metric.

Equality of Opportunity: Persons that are fully qualified for an outcome shouldn’t see their outcome changing based on their belonging to a subgroup.
Predictive Parity: If a classifier had favored an outcome for a subgroup, the hypothesis should be true in the real life.

an outcome for a subgroup, the hypothesis should be be true in the real life.

Those definitions of fairness and parity are not exhaustive.
A recent research paper shows that they are fundamentally in conflict and cannot be satisfied simultaneously.

Fairness of Representation: Word Embeddings

Word embeddings are widely used in Natural Language Processing to represent word syntactically and semantically .

They have an interesting linear property that allows
→ vector(man) — vector(woman) = vector(king) — vector(queen) .
→ vector(man) — vector(woman) = vector(men) — vector(women) .

Besides their efficiency Word embeddings can propagate some very dangerous biases based on the training data — such as Gender Stereotyping:

→ vector(man) — vector(woman) = vector(developer) — vector(designer) .

Debiasing word embeddings

There are two types of Gender Stereotyping:

A recent paper, available, introduced a novel approach to de-bias Word Embeddings using Crowdsourcing.

Here is briefly how would take it away, with our previous gender bias example:

>First: Gather all words X and Y words of the vocabulary such as:
→ (i) vector(she)-vector(he) ≈ vector(X)-vector(Y)
→(ii) X-Y is an unappropriated gender stereotype

>Second: Build a matrix W of the (X-Y) pairs.

>Third: Take the singular value decomposition of W to find the subspace corresponding to gender stereotyping. Most of the time, these singular values are very skewed, and it is easy to select the top one or the top two singular vectors to represent this bias subspace.

>Fourth: Apply the bias correction:

Word Embedding De-biasing Results:
 — Reduced gender stereotyping in the word embeddings
 — Performance on downstream tasks still almost the same

Main takeaways:
>Supervised learning:
 — Maximize the accuracy of a classifier subject to fairness constraints
 — Retrain the classifier to satisfy fairness constraints.
>Unsupervised learning:
 — Ensure features learned are not propagating training data biases.

More resources about Deep Learning:
We recently launched a series of courses in Deep Learning from theory to deployment: https://github.com/Riminder/deep-learning-practical-course
> Course 1 (05–04–18):
— Introduction to Deep Learning — Mouhidine SEIV (Riminder)
> Course 2 (12–04–18):
— Deep Learning in Computer Vision — Slim FRIKHA (Riminder)
> Course 3 (19–04–18):
 — Deep Learning in NLP — Paul COURSAUX (Riminder)
> Course 4 (02–05–18):
 — Introduction to Deep Learning Frameworks — Olivier MOINDROT (Stanford)
> Course 5 (10–05–18):
Efficient Methods and Compression for Deep Learning — Antoine BIARD (Reminiz)
> Course 6 (17–05–18):
 — Deployment in Production and Parallel Computing — INVITED GUEST

More ressources about Fairness in Machine Learning
>Measuring and Mitigating Unintended Bias in Text Classification (Dixon et al., AIES 2018)
— Exercise demonstrating Pinned AUC metric.
>Mitigating Unwanted Biases with Adversarial Learning (Zhang et al., AIES 2018)
— Exercise demonstrating Mitigating Unwanted Biases with Adversarial Learning.
>Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations (Beutel et al., FAT/ML 2017).
>No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World (Shankar et al., NIPS 2017 workshop)
>Equality of Opportunity in Supervised Learning (Hardt et al., NIPS 2016)
>Satisfying Real-world Goals with Dataset Constraints (Goh et al., NIPS 2016)
>Designing Fair Auctions:
— Fair Resource Allocation in a Volatile Marketplace (Bateni et al. EC 2016).
 — Reservation Exchange Markets for Internet Advertising (Goel et al., LIPics 2016).
>The Reel Truth: Women Aren’t Seen or Heard (Geena Davis Inclusion Quotient)

Recent talk at Serena Capital about Fairness in recruitment

What’s next for you?

• Learn more about our product: https://support.riminder.net
• Check out our uses-cases: http://support.riminder.net/category/bZA6ni6k6X-use-cases
• Discover our latest updates: https://updates.riminder.net

Are you a Developer?
You can start now using our self-service API without any painful setups.
Get started in few minutes with our documentation:
https://developers.riminder.net

If you enjoyed this article, it would really help if you hit recommend below :)

Follow us on Twitter @seivmouhidine , @othmaneizi1, and @riminderdotnet

If you want to work on AI + FAIRNESS, check our jobs page!

Overcoming recruitment biases was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

Kima, Serena, Kerala and Daphni and more join Riminder’s Marketplace to help their startups with recruiting

“Startups are people first and foremost. Every opportunity to better find and hire talent is a must have for any good team.” Jean de la Rochebrochard, Kima Ventures

Funds do not let friends hire without Riminder

Funds and talents are the sinews of war for startups.

At Riminder.net we’re very glad to announce our partnership with VC funds in order to boost the sourcing capabilities of their portfolio startups through our Marketplace. It allows VC funds go beyond the pure financial investments and help entrepreneurs access qualified applicants and close the talent gap.

Profiles Archetypes:

Funds’ marketplaces attract relevant talents across all seniority levels. The large majority are either former co-founders or startups employees:

• Data Scientists
• Frontend Developers
• Backend Engineers
• Devops Engineers
• Full Stack Developers
• Sales Managers
• Account Managers
• Community Managers
• Operations Managers
• VP Sales, CMO, etc.

The Kima Ventures Example:

1. Kima acquires Talents through the Riminder’s CVbot.
   The CVbot is a one line of code HTML custom integration that drastically simplifies the application experience by allowing prospective candidates to submit their resume with a simple drag&drop.
2. Kima shares qualified applications through the Riminder’s Marketplace
   To get access to the profiles all you need is the invite code from Kima.

Some facts:
On average, 95% of candidates spend less than 33 seconds on a career website. Only 3% of them complete an application . Stop missing out on the other 97%.

Join our Marketplace
If you are interested to know more about Marketplace, you can book us for demo : http://docs.riminder.net/marketplace .

How to get it up and running, for startups?

Get started in few minutes and access qualified applications:

Step 1: Create your an account

It takes less than 2 minutes https://www.riminder.net/create/team

Step 2: Obtain your access code

Are you interested in a particular marketplace? Request your access code now: https://riminder.typeform.com/to/AbH3xB

Step 3: Add your favorite Marketplace

Simply enter the name and the access code related to the Marketplace of your choice.

Step 4: Create your job filter

• Left: The job filter parameters that corresponds to your hiring needs.
• Right: The job insights show the market best practices.

From now on, the best talents are within your hands’ reach!

What’s next for you?

• Learn more about our product: https://support.riminder.net
• Checkout our uses-case: http://support.riminder.net/category/bZA6ni6k6X-use-cases
• Discover our latest updates: https://updates.riminder.net

Are you a Developer?
You can start now using our self-service API without any painful setups.
Get started in few minutes with our documentation:
https://developers.riminder.net

If you enjoyed this article, it would really help if you hit recommend below :)

Follow us on Twitter @seivmouhidine , @othmaneizi1, and @riminderdotnet

If you want to work on AI + FAIRNESS, check our jobs page!

Kima, Serena, Kerala and Daphni and more join Riminder’s Marketplace to help their startups with… was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

If you are working in the HR software industry or a member of an HR department, you definitely have thought about structuring your CV data before. Projects requiring resume structuring can be crucial to your business and can range from simple user experience improvement to strategic product roadmap advancements. These are some examples of the common use cases of CV structured data in the HR Software Industry:

• creating an efficient and relevant talent search experience
• getting market-relevant insights about your talent pools
• Building usable datasets for an AI-based job matching tool.

Resume Parsing, the inevitable solution to your problem

A resume parsing solution is a software that takes a resume as an input that can be in any media format (PDF, Word or image) or template, then convert it into a structured data format like — such as XML or JSON.

The information that is extracted by a resume parser usually includes the following:

personal information: name, email, address, phone

list of experience: start date, end date, location, job title, company, description, …

list of education: start date, end date, location, degree, university …

list of skills, …

list of interests

Seems easy? But the reality is hard!
No improvement for more +10 years

Here are some few metrics:

+1.4 Billion resumes are parsed every year.

+40% of resumes have a complex layout (multi-column,etc.)

+7% of resumes are either scans or images

The first resume parsers were born in the late '90s to provide a data structuring technology to HR software companies that are looking for a stand-alone packaged solution in order to focus on their core business. Some of these first-mover solutions are:

• Sovren (1996)
• TextKernel (2001)
• Daxtra (2002)
• …

How Daxtra, Sovren, Hireability, Textkernel and Segmentr (by Riminder) are doing at this task?

Building a general and reliable parser requires many building blocks.
For instance, the system should be able to handle:

• complex layouts (ex: multi-column resumes, pictures with backgrounds, etc.)
• ambiguous entities (ex: Facebook, as a former employer vs. a social media skill)
• different media formats (PDF, Word, Image, etc.)
• multiple languages
• etc.

The following comparison between some of Segmentr’s features and famous existing resume parsing solutions is the result of extensive validation tests we led at Riminder:

Segmentr (by HrFlow.ai) is the only Resume Parser able to handle such examples

We’ve also computed the performance of each solution over a validation dataset of around 100 resumes randomly sampled. For each output, we averaged the accuracy obtained across the multiple labels. Below is a graph summarizing the obtained results:

Segmentr example in python

First, you have to post your data using a POST REQUEST on following the endpoint below:

🧠 Parse a Resume in a Source

Here is the structure of the data that you’ll get:

📖 The Profile Object

What’s next for you?

Discover Segmentr Live
If you are interested to know more about Segmentr, you can book us for demo : https://hrflow.ai/book-us .

You can also visit our https://labs.hrflow.ai to see AI applied to HR in action.

Are you a Developer?
You can start now using our self-service API without any painful setups.
Get started in a few minutes with our documentation:

• 🤗 The Official HrFlow.ai Docs | HrFlow.ai Guide
• The official Blog of HrFlow.ai

If you enjoyed this article, it would really help if you hit recommend below :)

Follow us on Twitter @hrflowai

If you want to work on AI + FAIRNESS, check our jobs page!

HR software companies? Why structuring your data is crucial for your business? was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.

Les principales missions des directions des ressources humaines concernent le recrutement et la mobilité interne : trouver les candidats pertinents pour les postes à pourvoir, permettre l’évolution des salariés en leur proposant des parcours attractifs, et favoriser un environnement de travail épanouissant pour les collaborateurs.

Les résultats d’une étude que nous avons menée sur l’emploi montrent que + 31% de nouveaux métiers apparaissent chaque année. Selon la Harvard Business Review, + 60% des emplois actuels disparaîtront au cours des 2 prochaines décennies. Le défi majeur des entreprises, en passant par les recruteurs et les managers, est donc de qualifier, valoriser et développer le potentiel humain dans un contexte en hyperévolution.

La donnée ne parle jamais d’elle-même

Face à aux enjeux d’accélération actuels, les recruteurs ont besoin de «super-pouvoirs». L’intelligence artificielle basée sur la technologie «deep learning» (apprentissage profond) permet d’exploiter tout le potentiel des données non structurées liées à l’emploi et de fournir des indicateurs tangibles pour prendre les bonnes décisions. Étant inspirée du fonctionnement du cerveau humain, cette technique de «machine learning» (apprentissage automatique) permet d’atteindre une pertinence inégalée.

En quelques années, cette technologie a progressé de manière fulgurante. Prenons 2 exemples. En 2006, Microsoft tentait tant bien que mal de dicter un texte que l’ordinateur devait écrire tout seul dans Word, sans «deep learning». En 2012, avec leur assistant personnel Cortana, ils sont parvenus à traduire en temps réel un discours de l’Anglais au Mandarin. En 2016, ces algorithmes ont été à l’origine de la victoire historique d’AlphaGo développé par la société DeepMind (rachetée par Google) contre la légende vivante du jeu de Go, le Sud-Coréen Lee Sedol.

Aujourd’hui, nous avons la chance de vivre à une époque où le marché et les entreprises regorgent de données liées à l’emploi. Grâce aux techniques de «big data», il est possible de capturer, normaliser, stocker et rendre accessible la donnée. Cependant, pour en tirer le meilleur parti, il faut être capable de l’analyser. Comme le dit Prof. Ivar Ekeland (École Normale Supérieure) : «la donnée ne parle jamais d’elle-même, il faut trouver le moyen subtil de la faire parler». De plus, lorsque la taille de la donnée qu’on étudie est infiniment grande, on ne peut se fier ni à la recette, ni au flair, ni à l’intuition. La donnée a besoin de modèles mathématiques et d’algorithmes adaptés. Le «deep learning» est en train de révolutionner le fonctionnement des géants des technologies (Facebook, Google, Apple, Microsoft, IBM…), la fonction RH n’est pas une exception.

L’intelligence artificielle au service de l’humain

En s’appuyant sur des corrélations, nettement plus pertinentes que de simples recherches par mots-clés, cette technologie permet par exemple de : suivre l’évolution rapide du marché de l’emploi, prédire le prochain poste d’un candidat, évaluer sa candidature automatiquement à partir de son CV en analysant son parcours, ses expériences, ses compétences traverses, et en mesurant son adéquation avec la culture de l’entreprise. Loin de «cloner» les candidats, cette technologie offre de nouvelles perspectives de recrutement en favorisant la pertinence, la flexibilité, la diversité, la rapidité du processus et en apportant des preuves tangibles.

L’intelligence artificielle est un atout au service de l’humain. Elle n’entend pas remplacer l’intelligence humaine ou le rôle du recruteur. Elle joue le rôle auxiliaire d’une extension qui permettra d’augmenter les capacités du recruteur, comme les voitures sont aujourd’hui une extension pour nos jambes. Elle élargira sa vision en la confrontant à celle du marché, lui donnera accès à des indicateurs insoupçonnés et accroîtra la dimension stratégique de la fonction RH. Bref ! Elle lui permettra de se concentrer sur ce qui est de plus humain dans son métier.

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L’intelligence artificielle au service de l’emploi was originally published in HrFlow.ai (formely Riminder) on Medium, where people are continuing the conversation by highlighting and responding to this story.