When Resume Bots Meet HR Bots - Who wins?

AI can write your resume in seconds. AI can also reject your application in seconds. The hard part is the question nobody wants to own: when an algorithm screens people out, who is responsible for the damage it can cause?

We dig into the real-world legal and leadership stakes behind AI in hiring, including the warning shot from the Workday case and how automated scoring and sorting can create algorithmic bias, including age discrimination risk. From there, we get practical about what actually happens inside applicant tracking systems (ATS) and HR AI: pattern matching, keyword rules, knockout questions, and the hidden ways a job description can accidentally become a mass rejection tool. If you lead a team, we explain why “the system decided” is a dangerous sentence.

We also talk to job seekers navigating AI resume screening. We share ATS-safe resume formatting, why columns and graphics can break parsing, how keyword stuffing backfires, and why measurable results beat vague claims every time. And we end with the human side of the process: networking, referrals, and panel interviews that reduce bias and focus on real job skills instead of wordsmithing.

Our bottom line is Management By Responsibility. AI can assist, but leadership must define standards, audit outcomes, review false negatives, and sign off on the process. Subscribe, share this with a hiring manager or job seeker, and leave a review. What part of hiring should never be automated?

Transcript

Welcome And The Big Question

SPEAKER_01 0:13

Well, hello, and welcome to our podcast, Success, Secrets, and Stories. I'm your host, John Wondolowski, and I'm here with my co-host and friend, Greg Powell. Greg? Hey everybody. And when we put together this podcast, we wanted to put out a helping hand and help that next generation and help answer the question of what does it mean to be a leader? Today we want to talk about a subject that I think supports that concept.

Workday Lawsuit And AI Liability

SPEAKER_01 0:48

So if AI generates your resume and AI and the HR department judges your application, which AI system wins? In a recent court case, a federal judge ruled that workday could be liable for age discrimination under the Age Discrimination Employment Act. The district judge Rita Lynn determined that the software vendors can be legally accountable as agents of the employer if their automated tools actively score, sort, and reject candidates. But it doesn't end there. The basic truth about all AI programs for human resources is that it's a process that discovers patterns. In the application's resumes, then it is treated as issues related towards employment. That's how they all usually work. And like any other program, humans are involved in creating the software approaches used to read resumes. It is human nature to describe what is desirable in a candidate for a position. The problem is AI, artificial intelligence. The program has the ability to go beyond basic instructions. AI is designed to look for patterns, to search for data across a number of different databases. As with any of these systems, the software may go beyond the internal documentation and pull data that is publicly available on the internet. And here's where it starts to get tricky. It's hard for a computer. It doesn't have a sense of responsibility. It's a machine that is limited by data that it finds. Whether the data is correct, whether it is intentionally incorrect, or simply shaped by the opinions of the person who wrote the software in the first place.

Hiring Before Automation Took Over

SPEAKER_00 2:50

So going back in my ancient HR history, I recall being a recruiter placing a large display ad in the Sunday newspaper, directing applicants to send their hard copy resumes to a mailbox in our business office so I could review each resume personally. Oftentimes reading resumes late nights and weekends. It was a very slow and a very painstaking process. The good news was we had applicants and plenty of them. But the bad news was chauffeurs and chefs were applying for jobs they weren't remotely qualified for. The notion was that our company was hiring, and if you could get your resume in the building, you had a shot at getting considered for a job opportunity somewhere in the company. So let's fast forward up to today.

Where AI Helps And Where It Fails

SPEAKER_00 3:37

Technology has an automated tool that can be very helpful in the resume screening process if used appropriately and with oversight. There absolutely has to be some human intervention in the review process of resumes. AI created resumes reviewed by AI and a final hiring decision by AI. That really doesn't sound right, does it? I have to admit that concern is exactly why we gave very specific instructions using any software system reviewing the resume. Let me explain the parameters we asked the program to evaluate. And here is the important part that I really need to clarify. The resume software evaluation was influenced by several go-no-go answers as to whether the candidate met the basic requirements or the minimum requirements of the job. For example, imagine receiving 2,000 resumes for 30 summer internship positions. The AI tool was invaluable in culling through that volume of applicants in a timely fashion so we could focus on those applicants that met or exceeded our criteria. But in situations where we're looking at a modest number of applications, we still use the keyword search feature and the go, no-go, or that knockout question feature in the human resource information system. So, for example, one requirement for a position might be listed as 10 plus years of experience in a management role with profit and loss responsibility. Or maybe the education requirement might be Bachelor of Science degree in engineering or related field tied to production or manufacturing. The HR program was only intended to weed out resumes that did not meet the minimum standards required to be eligible for the job. Let me ask you one other question.

Job Descriptions And Vendor Risk

SPEAKER_01 5:15

Wasn't the job description probably one of the most important things to help in that process? Isn't there also a connect to a job description?

SPEAKER_00 5:24

Absolutely, John. The job description has to be right on for what's really needed for the role.

SPEAKER_01 5:30

Those are all the tools that HR is using. And it's a real challenge for management teams to use a vendor's product to avoid direct involvement in decision making that is related to employment. At the same time, you may also be handing over your legal exposure to a group of computer programmers that maybe never spent a day in the HR department. And the ramifications are on the HR department technically, not on the programmer. The real challenge here is the term artificial intelligence. You may think you gave the software enough of instructions to make a sound decision, but whether a candidate shooter should not be moving forward is really a process. And the confidence in that software can be misleading.

SPEAKER_00 6:21

So AI and the vendors creating it can face significant legal exposure if algorithmic bias occurs. That bias may surface as ethnic or gender bias, eliminating quality candidates, or putting the company in harm's way from a legal standpoint. So when you allow AI to help decide whether to hire an employee, you had better get a detailed list showing where your assumptions were, where they came from, and what was judged unacceptable before you ever set that resume to the side. Assuming the software is applying your requirements correctly is exactly the kind of assumption that can come back and bite you at the end of the day.

The Keyword Game And Resume Bots

SPEAKER_00 6:57

We've all seen it. When you're writing a resume, the AI systems will make recommendations that would customize your resume to meet precisely the employer's posted job requirements. And then the game begins.

SPEAKER_01 7:10

Yeah, and programmers love that term custom. And I've seen these programs used for both the candidate and for the hiring manager. And the problems are constant. The challenge is what keywords are required for the position, whether it's in your job description or whether it's in the document you sent originally to HR for an opening, it becomes an alphabet soup of terms that employers expect to see on a resume. The problem is that trying to describe all these different experiences you had through your life to match the majority of the keywords on a posting does not fit on one page. Resumes that are two or three pages long are prone upon. So I've heard one of the tricks that was used is creating an outline if you would attach your resume. Yeah, that's not really a great idea. In essence, they had another version where you had the word soup of words that mean something, and it's like a computer-generated keyword, I don't know, art. And it is just as confusing and really not a good idea.

SPEAKER_00 8:25

And the sophistication of an AI-genated resume can turn into a conglomeration of other people's resumes, right? Making your presentation sound really generic and then it loses its personality. One computer is talking to another computer, hoping to get enough attention to allow the paperwork to move forward. So when I was in situations where I was frustrated with the limited number of resumes we received for certain jobs, I would go back to my team and challenge them to physically review all the resumes as well as investigate all of our sources used to find candidates. We needed that human touch. For the most part, the software did a very good job of limiting the marginal candidates who should not have even applied in the first place. But there were occasions when I saw individuals who clearly demonstrated skills and abilities on the resume, yet they did not word their experience in the same way we wrote the requirements. People with profit and loss responsibility in architectural roles or technical support tied to engineering could be eliminated simply because they did not have the exact degree or word loaded into the software.

SPEAKER_01 9:30

So we've talked about this subject before on other podcasts. And I know that we've discussed the ATS system, the application tracking system. As a tool, Greg, that you've used in the past, do you see that tool changing over time? Do you think AI systems have replaced ATS systems?

SPEAKER_00 9:50

So the ATA systems is the approach used to give technical parameters to the software in the department's resume review process.

ATS Proof Formatting And Specific Metrics

SPEAKER_00 9:57

What you're really doing is asking the software to strategically match job description keywords. But an important element in searching for a new employee is still the network of personal references received from the candidates themselves. Let me list some of the things that AI system or an ATS system might reject or struggle to recognize in a regular resume. Number one, always use a simple layout without columns. Don't use unusual spacing or decorative fonts that can confuse those software systems trying to recognize, again, those keywords and patterns. Eliminate the graphics, remove the personal photo, and avoid colored fonts. Keep the document clean and primarily black and white. The font should be easy to read and only artistic if you're applying for a job in a creative field. Personally a big fan of Ariel and Times New Roman because they are still among the most practical business fonts in use.

SPEAKER_01 10:52

And since we're talking about software and patterns, there are some strategic words that should be included. One is if they're looking for experienced people with people management skills, now's not the time to come up with new terms on being empowered or engaged. Use the exact words to get past the AIHR bulldogs. Two, avoid word stuffing. Love this concept. And what it basically is, is taking that computer and try to fool the other computer with words that would make the system comply. Whether they actually are relevant or not, you're just stuffing words in. Now, if you really want to be tricky, you do that word stuffing when it's excessive in white font so that only the computer systems see the extent of word stuffing. Love that sneaky approach. Measurable data is also another thing that makes computers happy. And it makes hiring managers happy. Don't be vague in terms of what the data really implies in terms of impact. Instead of saying that you've worked on energy management concepts, write a specific approach to reducing the electrical demand in your corporate office by 12%, saving $316,000. Computers and hiring managers love specific detail.

SPEAKER_00 12:28

And then there are obviously ways to bypass all these computer-based systems through personal contacts. A personal reference that gets someone to directly read your resume is still the very best way to minimize the impact of an AI-driven human resource information system. And people will say, I don't know anybody at that organization. But you know, you can search places like LinkedIn for people who are connected to the same network that you're connected to. You might have a family member, former classmate, professional contact, coworker who works for the company and can help you understand the opportunity and possibly create a path to that hiring manager directly.

SPEAKER_01 13:08

So

Management By Responsibility For Hiring

SPEAKER_01 13:09

let's add one more layer to that conversation, because I think there's a larger story that really comes together. If a company is using AI in hiring, then they need a really good software package. They need a management philosophy that defines who is responsible, who does make the decision, who reviews the exceptions, who owns the consequences of the process when it goes wrong. There's the big rub. You need MBR and its approach to add real value to the process.

SPEAKER_00 13:46

Exactly, John. Management by responsibility says you do not hide behind the system. You define responsibility at every single step. In the hiring process, that means the software can support the work, but it cannot become the excuse for the outcome. Somebody in human resources has to own the screening rules, someone in leadership has to approve that criteria, and someone has to audit the results. And of course, someone has to step up when the technology starts eliminating candidates for reasons that do not line up with the actual needs of the business. That approach changes the whole story because it puts accountability back where it belongs, on the people managing the process, not on the machine executing it.

SPEAKER_01 14:28

And as we talked about before, people have to get involved in the job description because that is a core component that HR uses. And to have something written 12 years ago is not relevant to a posting that you're actually putting out in the field. And that overall message is really the point of our podcast. We're not talking on whether AI is a smart or useful tool. We're talking about whether leaders are engaged. Management by responsibility reminds us that technology should support judgment and replace it. It requires leaders to establish decisions and clearly document that for the purposes of limiting automation, reviewing outcomes on a regular basis. A hiring tool is just that. It's a tool, and it's only as good as the people that are using it.

SPEAKER_00 15:24

Practically speaking, that means setting up safeguards. It means creating hiring standards that are very specific, job-related, and legally defensible. It means testing the system to see who is being screened out and why. It means reviewing false negatives where strong candidates were rejected. It means assigning a person, not a platform, to sign off on the final process. One of the strengths of management by responsibility is that it forces organizations to separate assistance from authority. AI can assist, AI can summarize, and AI can even flag patterns. But authority still belongs to leadership. Responsibility still belongs to the people accountable for the decisions. One distinction can save a company from legal problems, poor hires, and the kind of detached leadership that says efficiency matters more than fairness.

SPEAKER_01 16:20

And that really is the key. Applicants should not be judged by an imaginary or invisible filter. We need to be engaged in the process, both uh being sensitive to people who are putting in their resumes and being sensitive to how it's going to be actually utilized. Managers have to be careful in terms of just simply making lazy assumptions. HR becomes much more effective because it doesn't simply depend on software outputs. It should be an engaging process with management, a process that shows visible ownership and measurable standards.

SPEAKER_00 16:57

So if we breathe this full circle, the impact of management by responsibility is simple but powerful. It restores ownership. It says every automated process still needs a human framework of accountability. It says that leaders must define expectations, monitor outcomes, challenge bad assumptions, and correct the system when it starts making poor choices. And in a world where AI is increasingly shaping resumes, screening candidates, and influencing who gets seen, that may be the most important takeaway of all time. The future of hiring should not be built on convenience alone. It should be built on responsibility. Because when responsibility is clear, technology becomes a tool for better judgment instead of a shield for bad decisions.

SPEAKER_01 17:44

So let's just take that

Panel Interviews And Hiring Teams

SPEAKER_01 17:45

one step further. And you have your top three or four candidates, and now you're ready to move on to the point of actually hiring someone. I think it's important to talk about that because the hiring process is more than just the resume and what is being brought forward. In my experience, conducting a panel interview is actually a great way to interact with the HR department and evaluate candidates overall to be effective. The panel reduces bias, speeds up the hiring process, aligns technical needs, and also engages in company culture. This cooperative approach actually ensures compliance and helps direct a team assess actual job skills, not the wordsmith, and the requirements that you need in order to fill those open positions. The concept of a hiring team involves leadership members that are basically making that bottom-line decision. Think about it. You're building trust with your staff when you do this approach, with your peers, and sometimes I would include the customers to share in the ownership of the new employees' success. The key point to this approach is that the team, the hiring team, can make recommendations, but the final decision remains on the hiring manager's responsibility. Hopefully, this discussion about responsibility and hiring new employees demonstrates the ethical and effective ways. And in the process, it talks about building a legacy of qualified staff and a growing management team. Most of all, it demonstrates leadership skills for the organization. The way that we hire people, how we treat our candidates, is how we see ourselves.

Resources Contact Info And Wrap

SPEAKER_01 19:32

So if you like what you've heard, I've written a book called Building Your Leadership Toolbox, and we talk about tools like this. And it's available on Amazon and Barnes and Noble and other sites. The podcast is what you've been listening to. Thank you so much. It's also available on Apple, Google, and Spotify. A lot of what we talk about is with Dr. Durst and his MBR program. If you'd like to know more about Dr. Durst, you can find out on SuccessGrowthAcademy.com. And if you'd like to contact us, please send me a line. That's wando seventy-five periodjw at gmail.com. And the music has been brought to you by my grandson. So we want to hear from you. Drop me a line. Tell me what's going on, what you like, and what you would like to hear about. It has always helped us to create content. Thanks, Greg. This was fun. Thanks, John. As always. Next time.

SPEAKER_00 20:32

Yeah.